Contextual Advertising

New technologic environments secure to their users sophisticated exit routes to
intrusive publicity. There is, actually a crescent recognition about the sophistication of
consumers and the complex relationship between propaganda on the digital world. Such a position is supported by 200 million users of Ad blocking software estimated throughout the world. It seems clear that relevant, smart and attractive productions are key pieces to perpetrate advertising as a business in this environment. However, how does the social networks user synthesize these concepts? What are the motives that lead users of digital media to expose themselves to advertising messages? In view of the fact that the nature of the advertisements consumption requires a high degree of interest from the audience member for their exposure, we have conducted a study in Brazil and Portugal, developed by applying the method of Grounded Theory, firmly rooted in one hundred and eighteen indepth interviews conducted in 2016 and 2017. The text is dedicated to introduce the three most consistent motivations categories towards exposure to advertisement, A. Information/Surveillance B. Humour Management and C. Personal Integration.

Designing effective prompts for the aim of getting valuable outputs from artificial intelligence systems is the focus of the emerging field of prompt engineering, which is focused with the design of prompts. The development of artificial intelligence has resulted in the emergence of a demand for trained quick engineers who are capable of maximising interactions between humans and AI, particularly in the field of natural language processing. After conducting research and conducting analysis, the author of this essay comes to the conclusion that India possesses the necessary resources to develop educational programmes and cultivate talent in order to become a global leader in rapid engineering. There is a creative potential in the interactions that occur between computer science, psychology, language, and prompt engineering. Even though it is present on a global scale, businesses are beginning to recognise the potential of artificial intelligence (AI) when it is provided with the appropriate guidance. This has resulted in an expected annual growth of over twenty percent in the need for quick engineering. The country of India is in an excellent position to capitalise on this opportunity because it possesses a large pool of qualified engineers. The development of huge language models is receiving a lot of attention from a lot of people, and they are making significant progress in this area. One of the primary causes is the introduction of OpenAI's ChatGPT models, specifically the GPT3.5-turbo and the GPT-4. A innovative approach to the production of synthetic data and the distillation of knowledge is presented in this article through the utilisation of fast engineering. The three approaches that are the primary focus of our attention are the fundamental, the composite, and the similarity approaches. One of the most common challenges that machine learning applications face is dealing with imbalanced datasets, and the objective of this research is to investigate the capabilities of various algorithms to deal with such datasets. When compared to the entire dataset, not a single one of the strategies that are based on prompts performs adequately in the studies. On the other hand, the method of similarity prompting is superior to the other approaches and has a great deal of growth potential. According to the findings, there appears to be a significant opportunity to enhance these technologies and provide synthetic data that has a greater diversity of variables.

This study examines the effects of advertising divergence, relevance, and customer-related factors on meaning creation in conventional and creative advertisements. Using quantitative analysis with Structural Equation modelling (SEM) and Confirmatory factor analysis (CFA) by SmartPLS, the research found that customer-related factors show a positive significant effect on meaning creation in both ad types, with a stronger positive effect in creative ads. Divergence strongly influences meaning creation in both ad types positively, particularly in creative ads. Customer-related factors moderate positively the relationship between divergence and meaning creation in both ad types. While relevance showed no significant direct impact on meaning creation. Finally, there is an interaction effect for customer related factors with relevance on meaning creation. These findings contribute to understanding the dynamics of advertising effectiveness and consumer perception.

Weblogs, comprised of records detailing user activities on any website, offer valuable insights into user preferences, behavior, and interests. Numerous recommendation algorithms, employing strategies such as collaborative filtering, content-based filtering, and hybrid methods, leverage the data mined through these weblogs to provide personalized recommendations to users. Despite the abundance of information available in these weblogs, identifying and extracting pertinent information and key features from them necessitate extensive engineering endeavors. The intricate nature of the data also poses a challenge for interpretation, especially for non-experts. In this study, we introduce a sophisticated and interactive recommendation framework denoted as InteraRec, which diverges from conventional approaches that exclusively depend on weblogs for recommendation generation. InteraRec framework captures high-frequency screenshots of web pages as users navigate through a website. Leveraging state-of-the-art multimodal large language models (MLLMs), it extracts valuable insights into user preferences from these screenshots by generating a textual summary based on predefined keywords. Subsequently, an LLM-integrated optimization setup utilizes this summary to generate tailored recommendations. Furthermore, we explore the integration of session-based recommendation systems into the InteraRec framework, aiming to enhance its overall performance. Finally, we curate a new dataset comprising of screenshots from product web pages on the Amazon website for the validation of the InteraRec framework. Detailed experiments demonstrate the efficacy of the InteraRec framework in delivering valuable and personalized recommendations tailored to individual user preferences.

Until now, no study has proposed strategies for social media advertising, scrutinizing the differential interaction of user involvement, different kinds of user involvement, product category involvement, and advertising appeals considering the unique characteristics of social media users in the Middle East. The present study addresses this issue by introducing two detailed advertising effectiveness grids in 20 situations for social media advertising in the Middle East context (Iran) and revised the previous grids and findings proposed for Western and Eastern countries in the area of traditional media advertising effectiveness. In all, 552 students of the University of Tehran, who are social media users were randomly selected, and questionnaires were distributed via an online platform. In order to explore the hypotheses, a mixed 2 × 2 × 2 factorial design was employed, followed by assessment of the validity and reliability of the measures. Surprisingly, some findings were contrary to...

Sponsored search represents a major source of revenue for web search engines. This popular advertising model brings a unique possibility for advertisers to target users' immediate intent communicated through a search query, usually by displaying their ads alongside organic search results for queries deemed relevant to their products or services. However, due to a large number of unique queries it is challenging for advertisers to identify all such relevant queries. For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on. We present a novel advanced matching approach based on the idea of semantic embeddings of queries and ads. The embeddings were learned using a large data set of user search sessions, consisting of search queries, clicked ads and search links, while utilizing contextual information such as dwell time and skipped ads. To address the large-scale nature of our problem, both in terms of data and vocabulary size, we propose a novel distributed algorithm for training of the embeddings. Finally, we present an approach for overcoming a cold-start problem associated with new ads and queries. We report results of editorial evaluation and online tests on actual search traffic. The results show that our approach significantly outperforms baselines in terms of relevance, coverage, and incremental revenue. Lastly, we opensource learned query embeddings to be used by researchers in computational advertising and related fields.

The research is devoted to the study of the functioning of paremias in the discourse of modern French social advertising. The relevance of the problem under consideration lies in the insufficient knowledge of the representation features of the French language paroemiological fund in modern public Internet advertising published in France. The utmost aim of the paper is to describe the main constitutive and linguistic features of modern French public Internet advertising and the specifics of the realisation of language paremiological units in it. The research material includes 312 objects of French public advertising distributed on the Internet in the period from 2016 to 2022. The study used methods of comparative, lexicographic and statistical analysis, as well as the method of semantic analysis of linguistic units. The results revealed that the use of paroemias as figure of speech in modern French public advertising is insignificant, due to the fact that the semantically complex message of the original proverbs and sayings can significantly narrow the target audience of public advertising. The use of paroemias as an element of public advertising (In its original, partially or completely modified form) makes it more intellectual and attractive directly to native French speakers.

Purpose-The purpose of this study is to review and synthesize the literature on in-app advertising, identify gaps and propose future research directions. Design/methodology/approach-The authors use a systematic literature review (SLR) approach, following the PRISMA guidelines, to investigate the current state of research in in-app advertising. The study uses 44 shortlisted articles from the Scopus and Web of Science databases. Using the Theory-Context-Characteristics-Methodology (TCCM) framework, the authors analyze the gaps in theory, context, characteristics and methods. Findings-Using thematic analysis, the authors identify five main themes in the in-app advertising literature, namely, ad platform optimization; mobile app user psychology and behavior; ad effectiveness; ad fraud; and security, privacy and other user concerns. The findings show the need for empirical research, with a strong theoretical foundation in emerging ad formats of in-app advertising, user behavior and buy-side of inapp advertising. Originality/value-This is a maiden study to conduct a domain-based SLR in the emerging field of in-app advertising using the TCCM framework. The authors highlight the key differences between in-app advertising and mobile web advertising. The authors propose theories in the advertising field that could be used in future empirical studies of in-app advertising.

For businesses to reach their target group, contextual advertising is becoming more and more important. But traditional methods often use separate steps for creating ad content and keyword targeting, which wastes time and makes ads work less well than they could. This article talks about a new method called Contextual Ad Generation and Targeting (CAGT). It uses Large Language Models (LLMs) to create ad content and improve keyword targeting all at the same time, within a single structure. To make ads more relevant and useful, CAGT uses adaptable prompt engineering and reinforcement learning-based feedback loops. CAGT increases the click-through rate (CTR) by 23% and the conversion rate (CR) by 18% compared to traditional methods. These results were found using a dataset of 10 million ad impressions from a major e-commerce site. When CAGT was used in a commercial ad network, it showed even better results, with an average increase in click-through rate (CTR) of 27% and an increase in clickthrough rate (CR) of 21% across 50 advertising programs over 3 months. The results show that LLMs could change contextual advertising by making ads more interesting and improving targeting all at the same time.

This dissertation researched impact of online advertising on daily users of mobile internet in Malaysia. Advertising using internet is a new platform for marketers to create awareness, increase brand loyalty and to identify the customers through their daily feedback. The aim of this paper is to examine the impact of online advertising on daily mobile internet users. Four features of online advertising are discussed and examined in the research, irritation, updating, respond, and personality of online advertisements to make the daily mobile users positive or negative thinkers and make them customers. This study involving a survey of 100 sample size. The dataset underwent a statistical analysis, i.e., structural equation modeling (SEM). Findings reveal that features of online advertising do generate positive influence on purchase intentions. Results further reveal that the Pictures feature generates the highest possibility of consumers’ purchase intentions. Marketers will find these r...

Web advertising (Online advertising), a form of advertising that uses the World Wide We b t o a t t r a c t c u s t o me r s , h a s b e c o me o n e o f t h e w o r l d ' s mo s t i mp o r t a n t ma r k e t i n g c h a n n e l s. T h i s paper addresses the mechanism of Content-based advertising (Contextual advertising), which refers to the assignment of relevant ads to a generic web page, e.g. a blog post. As blogs become a platform for expressing personal opinion, they naturally contain various kinds of expressions, including both facts and comments of both a positive and negative nature. Besides, in line with the major tenet of Web 2.0 (i.e., user-centric), we believe that the web-site owners would be willing to be in charge of the ads which are positively related to their contents. Hence, in this paper, we propose the utilization of sentiment detection to improve Web-based contextual advertising. The proposed SOCA (Sentiment-Oriented Contextual Advertising) framework aims to combine contextual advertising matching with sentiment analysis to select ads that are related to the positive (and neutral) aspects of a blog and rank them according to their relevance. We experimentally validate our approach using a set of data that includes both real ads and actual blog pages. The results indicate that our proposed method can effectively identify those ads that are positively correlated with the given blog pages.

Semantic similarity has become an important tool and widely been used to solve traditional Information Retrieval problems. This study adopts ontology of computer science and proposes an ontology indexing weight based on Wu and Palmer's edge counting measure and uses the N-grams method for computing a family of word similarity. The study also compares the subsumption weight between Hliaoutakis and Nicola's weight and query keywords (Decision Making, Genetic Algorithm, Machine Learning, Heuristic). A probability value (p-values) from the t-test (p = 0.105) is higher 0.05, which indicates the evidence of no of no significant differences between the two weights methods. The experimental results show the new keyword-keyword similarity matrix scores that compute from hierarchical relationship weight based on Computer Science ontology and string matching (tri-grams) for computing of string of keyword. We computed the document-document similarity matrix scores using our keyword similarity matrix scores and compared them with the keyword matching weights using Dice coefficient method. In addition, this paper, we presented a new document semantic ranking process for the semantic ranking that proposes a new weight of query term in the document based on Computer Science Ontology weight. The experimental results show that the new document similarity score between a user's query and the paper suggests that the new measures were effectively ranked.

Recommendation systems have been developed from the web. These recommendation systems are useful in collecting information from an available set of sources for a user's preferences. The information can be acquired from user's collection of details to share, to review, to do positive ratings by monitoring the user's behavior to improve the quality of top 'N' recommendations. Now if we come to modern learning system, it has good framework to influence the training factors from the data, triggers, and learner's preferences. Modern learning can be compared to online learning which carries to the future needs. Modern learning can be instituted in schools, engineering colleges, and working campus. The modern learning system combines interrelated data, processes, and resources to create a system of interdependencies that work together, adapting to changing business needs. These interdependencies include multi-level dynamics driven by the organization, training professionals, technological advances, and the learners themselves.

Recommendation systems have been developed from the web. These recommendation systems are useful in collecting information from an available set of sources for a user's preferences. The information can be acquired from user's collection of details to share, to review, to do positive ratings by monitoring the user's behavior to improve the quality of top 'N' recommendations. Now if we come to modern learning system, it has good framework to influence the training factors from the data, triggers, and learner's preferences. Modern learning can be compared to online learning which carries to the future needs. Modern learning can be instituted in schools, engineering colleges, and working campus. The modern learning system combines interrelated data, processes, and resources to create a system of interdependencies that work together, adapting to changing business needs. These interdependencies include multi-level dynamics driven by the organization, training professionals, technological advances, and the learners themselves.

As a prevalent type of Web advertising, contextual advertising refers to the placement of the most relevant ads into a Web page, so as to increase the number of ad-clicks. However, some problems of homonymy and polysemy, low intersection of keywords etc., can lead to the selection of irrelevant ads for a page. In this paper, we present a new contextual advertising approach to overcome the problems, which uses Wikipedia concept and category information to enrich the content representation of an ad (or a page). First, we map each ad and page into a keyword vector, a concept vector and a category vector. Next, we select the relevant ads for a given page based on a similarity metric that combines the above three feature vectors together. Last, we evaluate our approach by using real ads, pages, as well as a great number of concepts and categories of Wikipedia. Experimental results show that our approach can improve the precision of ads-selection effectively.

In the last decades the availability of portable devices as well as of internet accesses provided such great changes in people’s habits in taking advantage of media contents. Nowadays people are exposed to advertising through several media, such as newspapers, websites, and mobile/tablet applications . To establish which media might be more effective in terms of ads memorization is an ardous challange, but the answer has a great importance for strategic media planning of advertising communication. Some studies have already shown that media via paper are more effective than media via internet and websites in promoting the recall and the recognition of the ads [1],[2]. However, the scientific debate about the effectiveness of some media over the others is still an open issue.

This chapter presents a meta-search approach, meant to deliver bibliography from the internet, according to trainees’ results obtained at an e-assessment task. The bibliography consists of web pages related to the knowledge gaps of the trainees. The meta-search engine is part of an education recommender system, attached to an e-assessment application for project management knowledge. Meta-search means that, for a specific query (or mistake made by the trainee), several search mechanisms for suitable bibliography (further reading) could be applied. The lists of results delivered by the standard search mechanisms are used to build thematically homogenous groups using an ontology-based clustering algorithm. The clustering process uses an educational ontology and WordNet lexical database to create its categories. The research is presented in the context of recommender systems and their various applications to the education domain.

The online advertisement publishing industry is a rapidly growing multi-billion dollar industry. Organisations in this industry generate revenue by creating and/or acquiring online advertising space that they can sell at a profit. Their success is directly dependent on the effectiveness of their publishing strategy in terms of its ability to create traffic and interest for their clients by delivering advertisements which are closely in line with the recipient's interests. We propose a supervised learning based ad targeting technique which will help online advertisement publishers achieve this goal. Empirical tests of the new technique are very promising.

Recommender systems are systems that suggest items to the users of the web to help them find most relevant items of interest. These recommended items could include a movie, an image, query suggestions, tag recommendation etc. Data sources are modelled as graphs and are subjected to a naive diffusion method called heat diffusion method. The heat diffusion method identifies the similarities between different nodes to suggest recommendations. This method is applied to query suggestion which is a technique used to provide related queries to the user’s search query. A query-URL bipartite graph is constructed which is then mined to generate top-K recommendations for the query. In order to improve the search results, the heat values of the URL are also taken into account during the ranking of results. This technique is also extended to image recommendation.

This dissertation researched impact of online advertising on daily users of mobile internet in Malaysia. Advertising using internet is a new platform for marketers to create awareness, increase brand loyalty and to identify the customers through their daily feedback. The aim of this paper is to examine the impact of online advertising on daily mobile internet users. Four features of online advertising are discussed and examined in the research, irritation, updating, respond, and personality of online advertisements to make the daily mobile users positive or negative thinkers and make them customers. This study involving a survey of 100 sample size. The dataset underwent a statistical analysis, i.e., structural equation modeling (SEM). Findings reveal that features of online advertising do generate positive influence on purchase intentions. Results further reveal that the Pictures feature generates the highest possibility of consumers’ purchase intentions. Marketers will find these r...

Reseña del libro de John Battelle 'The Search: How Google and Its Rivals Rewrote the Rules of Business and Transformed Our Culture' (Nueva York: Portfolio, 2005)

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A review of John Battelle's book "The Search: How Google and Its Rivals Rewrote the Rules of Business and Transformed Our Culture" (New York: Portfolio, 2005).

Penelitian ini dimaksudkan untuk meneliti ciri-ciri berbicara wanita yang digunakan oleh pemeran utama dalam “Lucy” Luc Besson’s Movie. Ciri-ciri berbicara wanita adalah ciri-ciri yang secara rinci menunjukkan karakteristik bahasa wanita dan menunjukkan perbedaan penggunaan bahasa antara pria dan wanita. Karena pemeran utama pada movie ini adalah perempuan, penulis memutuskan untuk meneliti penggunaan ciri-ciri berbicara wanita oleh pemeran utama dalam “Lucy” movie dan tipe dari ciri-ciri bahasa wanita yang paling sering digunakan oleh pemeran utama pada movie ini. Penelitian ini dilakukan menggunakan metode descriptive. Data diambil dari skrip yaitu kata, frase, klausa, dan kalimat yang digunakan oleh pemeran utama pada movie. Kemudian data diteliti dengan mengidentifikasi dan mengklasifikasikan sepuluh tipe dari ciri-ciri berbicara wanita yang telah ditetapkan oleh Lakoff. Hasilnya menunjukkan bahwa tidak semua ciri-ciri berbicara wanita terjadi dalam dialog. Hanya ada Enam tipe d...

Classical contextual advertising systems suggest suitable ads to a given webpage just analyzing its content, without relying on further information. We claim that adding some information extracted by semantically related pages can improve the overall performances. To this end, this paper proposes an experimental study aimed at verifying to which extent the analysis of related links, i.e., inlinks and outlinks, can help contextual advertising. Experiments have been performed on about 15000 webpages extracted by DMoz. Results show that the adoption of related links significantly improves the performance of the adopted baseline system.

Information Filtering deals with the problem of selecting relevant information for a given user, according to her/his preferences and interests. In this chapter, the authors consider two ways of performing information filtering: recommendation and contextual advertising. In particular, they study and analyze them according to a unified view. In fact, the task of suggesting an advertisement to a Web page can be viewed as the task of recommending an item (the advertisement) to a user (the Web page), and vice versa. Starting from this insight, the authors propose a content-based recommender system based on a generic solution for contextual advertising and a hybrid contextual advertising system based on a generic hybrid recommender system. Relevant case studies have been considered (i.e., a photo recommender and a Web advertiser) with the goal of highlighting how the proposed approach works in practice. In both cases, results confirm the effectiveness of the proposed solutions.

A otimização dos recursos produtivos das empresas é de fundamental importância, visto a crescente degradação do meio ambiente, além da conscientização dos consumidores, que estão tendo preferência por produtos ecologicamente corretos. Neste contexto, surge o Marketing Ambiental como uma ferramenta que possibilita a exploração das práticas socioambientais por parte das organizações, gerando vantagem competitiva. O objetivo deste estudo é propor estratégias de marketing ambiental, com base na identificação do comportamento do consumidor em relação a práticas sustentáveis. Para tanto, realizou-se uma pesquisa exploratória e descritiva, de caráter quantitativo. Os dados foram coletados por meio de instrumento de pesquisa em plataforma online aplicado a 188 consumidores residentes na região do Vale do Jaguaribe. Os resultados demonstram que os consumidores estão em um estágio de transição, demandando mais informações acerca do assunto.

This paper describes a query classification system for a specialized domain. We take as a case study queries asked to a search engine of an art, cultural and history library and classify them against the library cataloguing categories. We show how click-through links, i.e., the links that a user clicks after submitting a query, can be exploited for extracting information useful to enrich the query as well as for creating the training set for a machine learning based classifier. Moreover, we show how Topic Model can be exploited to further enrich the query with hidden topics induced from the library meta-data. The experimental evaluations show that this system considerably outperforms a matching and ranking classification approach, where queries (and categories) were also enriched with similar information.

Постановка проблеми в загальному вигляді та її зв'язок з важливими науковими або практичними завданнями. У цифровому маркетингу значне місце займає інтернет-реклама, вагома частка якої базується на рекламній моделі з оплатою за клік PPC (pay per click). Компанії, які реалізовують товари чи послуги через вебресурси, часто замовляють налаштування платної реклами, яка допомагає залучити на цільову сторінку трафік та досягти бажаного рівня конверсії. В Україні попит на такі види послуг зростає, а ринок PPC динамічно розвивається. Постійно вдосконалюються технології управління рекламними кампаніями, використовуються нові інструменти наскрізної аналітики, аудиту цільових сторінок та оцінки рентабельності маркетингових інвестицій. PPC-агенції надають додаткові послуги з органічного просування вебресурсів та налаштування таргетованої реклами у соціальних мережах для досягнення максимальної ефективності усіх маркетингових заходів. Консалтингові компанії, агенції з маркетингових досліджень, студії цифрового маркетингу щорічно в кінці року визначають тренди та тенденції PPC, складають прогнози на наступний рік. Проте у 2022 році з початком війни в Україні алгоритм дещо змінився.

Web Scraping may consider as data theft action, several researchers have introduced some approaches for addressing this issue. These solutions could solve the problem in partial ways and sometimes, solution cannot be applicable with modern web techniques. Consequently, in our work we have introduced a new approach for stopping web scraping in an efficient way and applicable with modern web techniques called Markup Randomizer, which changes the HTML and CSS in proper way randomly in timely manner. The best feature of our model is that each web page can use it without paying any efforts or restrictions in web site markup. Experiments done over collected dataset which consist of 30 websites divided into three categories: News, Currency Rates and Weather. The proposed model based on Markup Randomizer applied over this dataset. The aim of the experimental is to measure the Similarity, File Size and the time. During testing the proposed model, we get that a change on the markup done up to...

Recommender systems are systems that suggest items to the users of the web to help them find most relevant items of interest. These recommended items could include a movie, an image, query suggestions, tag recommendation etc. Data sources are modelled as graphs and are subjected to a naive diffusion method called heat diffusion method. The heat diffusion method identifies the similarities between different nodes to suggest recommendations. This method is applied to query suggestion which is a technique used to provide related queries to the user’s search query. A query-URL bipartite graph is constructed which is then mined to generate top-K recommendations for the query. In order to improve the search results, the heat values of the URL are also taken into account during the ranking of results. This technique is also extended to image recommendation.

The aim of Recommender Systems is to help users to find items that they should appreciate from huge catalogues. In that field, collaborative filtering approaches can be distinguished from content-based ones. The former is based on a set of user ratings on items, while the latter uses item content descriptions and user thematic profiles. While collaborative filtering systems often result in better predictive performance, content-based filtering offers solutions to the limitations of collaborative filtering, as well as a natural way to interact with users. These complementary approaches thus motivate the design of hybrid systems. In this chapter, the main algorithmic methods used for recommender systems are presented in a state of the art. The evaluation of recommender systems is currently an important issue. The authors focus on two kinds of evaluations. The first one concerns the performance accuracy: several approaches are compared through experiments on two real movies rating data...

Today the evolution of the World Wide Web has brought us enormous and ever growing amounts of data and information.. The web is full of structured or unstructured information which is directly or indirectly influencing society or people in every day life. Due to huge data available with the web it has become a challenge for people with interest in utilizing this information. For mining the huge available data on web in the form of frequently used patterns and log files for meaning use is considered in this paper. The paper presents a preliminary review of Web content mining contributions in the field of web mining and the prominent successful algorithms and some tools.

The internet provides an alternative for product owners to advertise with new media. Web 2.0 is an advanced phase of Internet technology that allows users to contribute to Internet content, which ultimately makes the Internet interactive. This study provides an overview of how YouTube channel endorsements change the advertising process and patterns of interaction between advertising industry players. This study describes three patterns of interaction that occur in the advertising industry in the context of YouTube channel endorsement. Media Ecology Theory is used as a foundation for thinking, while the research model used is Case Study. Data were obtained through in-depth interviews with five sources who worked in the advertising industry. Three of them work in advertising agencies, and two other sources work as YouTubers, and in a Multi-Channel Network company. The research found how YouTube channel endorsement altered patterns of interaction and production processes in the advertising industry. YouTube channel endorsement also brought new players in the advertising industry, namely Multi-Channel Network.

In this globalization and industrialization era, in which many goods are produced in a very large amount every day, the existence of advertisements becomes out of burst too as its result. Companies utilize advertisements to sell their products. Therefore it is so easy for us to find out advertisements such as printed on newspapers and magazines we are reading, often appear on the internet while we are surfing, presented on the billboards in side of roads we are taking, and many more. In advertising, there is one element which holds an important role to make an advertisement can attract people’s attention. It is popularly called slogan The researcher used a descriptive approach method. The data are taken from internet slogan by taking 10 slogans best in Indonesia 2013 advertisement. They are: Acer, Asus, Apple, Dell, HP. Lenovo, Sony, Toshiba, LG, and MSI. In this research, the writer is considered as the main instrument that existence is determiner in collecting data. Conducting thi...

In this globalization and industrialization era, in which many goods are produced in a very large amount every day, the existence of advertisements becomes out of burst too as its result. Companies utilize advertisements to sell their products. Therefore it is so easy for us to find out advertisements such as printed on newspapers and magazines we are reading, often appear on the internet while we are surfing, presented on the billboards in side of roads we are taking, and many more. In advertising, there is one element which holds an important role to make an advertisement can attract people’s attention. It is popularly called slogan The researcher used a descriptive approach method. The data are taken from internet slogan by taking 10 slogans best in Indonesia 2013 advertisement. They are: Acer, Asus, Apple, Dell, HP. Lenovo, Sony, Toshiba, LG, and MSI. In this research, the writer is considered as the main instrument that existence is determiner in collecting data. Conducting thi...

Social networking has become a reality with presence of many sites such as Facebook, Twitter etc. They facilitatate people to have virtual communiies across the world and allow them to have communication through various means. Advertising in such web sites can provide maximum benefits to the companies. This paper presents a framework that supports semantic advertisement over such web sites based on the user preferences or favourites and interests by performaing mining user’s interactions in the social networking sites. Our framework can categorize social networking users based on the topic exchanges in the network and discover after mining the conversations as to which kind of advertisement can be placed in user’s pages automatically. Thus relevant advertisements which are meaningful to users as per their interests can be placed so as to attract the users. When compared to traditional advertising methods, this method improves or promotes businesses more and the cost of advertisement...

IJCRT1892165 International Journal of Creative Research Thoughts (IJCRT) www.ijcrt.org 1117 Analyzing Security Level of Web Links using Web Scrapping Tools and BeautifulSoup Environment Surjeet Kaur, S.R.Tandan, Priyanka Tripathi, Rohit Miri #4 #, Department of Computer Science and Engineering Dr. C V Raman University, Bilaspur, CG, India *, National Institute of Technical Teachers Training, Bhopal MP, India _____________________________________________________________________________________ Abstract—The way internet is used security is very important. All important notification available in the internet is in form of web links. Web scraping is used to extract URL using Python scripting and BeautifulSoup for proposed method. Security levels of links have been analyized on the basis of number of URL retrieved. Focused on method for URL retrieval technique has been developed and done on online data. URL extraction method has been implemented to classify the web links using Python and...

The objective is intelligent recommender system classification unit design using hybrid neural techniques. In particular, a neuroscience-based hybrid neural by Buabin (2011a) is introduced, explained, and examined for its potential in real world text document classification on the modapte version of the Reuters news text corpus. The so described neuroscience model (termed Hy-RNC) is fully integrated with a novel boosting algorithm to augment text document classification purposes. Hy-RNC outperforms existing works and opens up an entirely new research field in the area of machine learning. The main contribution of this book chapter is the provision of a step-by-step approach to modeling the hybrid system using underlying concepts such as boosting algorithms, recurrent neural networks, and hybrid neural systems. Results attained in the experiments show impressive performance by the hybrid neural classifier even with a minimal number of neurons in constituting structures.

The objective is intelligent recommender system classification unit design using hybrid neural techniques. In particular, a neuroscience-based hybrid neural by Buabin (2011a) is introduced, explained, and examined for its potential in real world text document classification on the modapte version of the Reuters news text corpus. The so described neuroscience model (termed Hy-RNC) is fully integrated with a novel boosting algorithm to augment text document classification purposes. Hy-RNC outperforms existing works and opens up an entirely new research field in the area of machine learning. The main contribution of this book chapter is the provision of a step-by-step approach to modeling the hybrid system using underlying concepts such as boosting algorithms, recurrent neural networks, and hybrid neural systems. Results attained in the experiments show impressive performance by the hybrid neural classifier even with a minimal number of neurons in constituting structures.

In this globalization and industrialization era, in which many goods are produced in a very large amount every day, the existence of advertisements becomes out of burst too as its result. Companies utilize advertisements to sell their products. Therefore it is so easy for us to find out advertisements such as printed on newspapers and magazines we are reading, often appear on the internet while we are surfing, presented on the billboards in side of roads we are taking, and many more. In advertising, there is one element which holds an important role to make an advertisement can attract people’s attention. It is popularly called slogan The researcher used a descriptive approach method. The data are taken from internet slogan by taking 10 slogans best in Indonesia 2013 advertisement. They are: Acer, Asus, Apple, Dell, HP. Lenovo, Sony, Toshiba, LG, and MSI. In this research, the writer is considered as the main instrument that existence is determiner in collecting data. Conducting thi...

The advent of the internet has revolutionized the field of advertising by providing a whole new path for reaching potential customers. Studies show that online advertising is, on the whole, extremely effective and that consumer acceptance of online advertising is comparable to traditional media[7][8]. One of the reasons for the high effectiveness of online advertising is that users interact with the web at a far more personal and intimate level than they interact with other advertising media like the radio or television. Pervasive computing environments deal with users at an even more intimate level; hence such environments are even better advertising platforms than the web. Pervasive environments allow the delivery of relevant advertising in suitable ways to selected consumers. In this paper, we examine some of the possibilities of pervasive advertising as well as some of the issues involved.

This paper extends previous research on the influence of social media and digital channels on customer purchase behaviour by presenting a new omnichannel purchasing model. We characterise that model as a “virtuous circle” as it centres around customer use of social media and has potential to benefit both customers and companies. We illustrate that model with a worked example, discuss approaches to its implementation and evaluate its use in the context of a business case study. The model creates a framework that combines elements of digital marketing, social CRM, omnichannel CRM, and customer experience and engagement. This paper bridges academic and industry practitioner communities across those fields.

Contextual Advertising (CA) refers to the placement of ads that are contextually related to the web page content. The science of CA deals with the task of finding advertising keywords from web pages. We present a different candidate selection method to extract advertising keywords from a web page. This method makes use of Part-of-Speech (POS) patterns that restrict the number of potential candidates a classifier has to handle. It fetches words/phrases that belong to the selected set of POS patterns. We design four systems based on chunking method and the features they use. These systems are trained on a naïve Bayes classifier with a set of web pages annotated with 'advertising' keywords. The systems can then find advertising keywords from previously unseen web pages. Empirical evaluation shows that systems using the proposed chunking method perform better than the systems using N-Gram based chunking. All improvements in the systems are found statistically significant at a 99% confidence interval.

This paper introduces a hidden topic-based framework for processing short and sparse documents (e.g., search result snippets, product descriptions, book/movie summaries, and advertising messages) on the Web. The framework focuses on solving two main challenges posed by these kinds of documents: 1) data sparseness and 2) synonyms/homonyms. The former leads to the lack of shared words and contexts among documents while the latter are big linguistic obstacles in natural language processing (NLP) and information retrieval (IR). The underlying idea of the framework is that common hidden topics discovered from large external data sets (universal data sets), when included, can make short documents less sparse and more topic-oriented. Furthermore, hidden topics from universal data sets help handle unseen data better. The proposed framework can also be applied for different natural languages and data domains. We carefully evaluated the framework by carrying out two experiments for two important online applications (Web search result classification and matching/ranking for contextual advertising) with large-scale universal data sets and we achieved significant results.

Background In forensic proficiency, the distinct model applied for age estimation includes physical examination, i.e., evaluation of somatic maturity and post pubescence peculiarities, dental development, and radiological assessment of skeletal maturity usually of the hand and wrist. The Greulich and Pyle (GP) method of skeletal age (SA) determination is considered quicker and easier with a lower error percentage of prediction. The specificity and applicability of the GP atlas have been recapitulated in many studies. This study aims to assess the applicability and reproducibility of the GP atlas on a sample of the eastern Utter Pradesh (UP) population. Results Considering the whole study population, the SA of the male subjects was retarded by 0.89 years or 9.03 months, whereas the SA of females were retarded by 0.36 years or 4.33 months than the chronological age, respectively. Conclusion According to this study, it is concluded that the GP atlas may not be applicable for both males and females in the eastern Uttar Pradesh region. The factors responsible for delayed skeletal growth and maturation may vary depending on demographics, ethnicity, and genetics. Further, a detailed study will be conclusive on a greater population size assessing more accurate and precise insights on the applicability and reproducibility of GP atlas.

This article describes how recommender systems are software applications or web portals that generate personalized preferences using information filtering techniques, with a goal to support decision-making of the users. Collaborative-based techniques are often used to predict the unknown preferences of the user based upon his past preferences or the preferences of the similar users that have already been identified. A user which has a high correlation with any group of users is known as white user whereas the users which have less correlation with any group of users are known as gray-sheep users. The presence of gray-sheep users affects the accuracy of the model, and generates inaccurate predictions. To improve the prediction accuracy, it is important to differentiate graysheep users from white users. Experimental results show that PowKMeans is effective in improving the prediction accuracy by 4.62%. It has also shown reduction in Mean Absolute Error by 0.7757.