Recommender System Based on User-generated Content

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

Online the idea of this paper is to give insights about a web-based project that aims to resolve the users query using machine learning technique of content-based filtering. The crux is to define the scope of the website. It becomes crucial to understand the business prospects of the project along with the relevance in the current technical world. As the paper goes about, various other papers and ideas will also be discussed that have been previously published and implemented. This paper will also break down the concept of content-based filtering for any individual who is a beginner in the field of machine learning.

Human beings, not machines, are the ultimate experts for information retrieval tasks, including recommender systems. Consequently, computers are most useful when they combine information about people's judgments. Collaborative filtering systems make use of this observation by having users explicitly rate items, such as Web pages, with the system making recommendations to other users based on overlapping areas of interest. A disadvantage of collaborative filtering, at least as currently implemented, is that it depends on users' explicitly entering data, which can be inconvenient and timeconsuming. We describe a recommender system, which we call ParaSite, that works by mining publicly-available hyperlinks on the Web, producing results competitive with the best text-based system.

Encyclopedia of Data Warehousing and Mining, Second Edition, 2009

In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They connect users with items to “consume” (purchase, view, listen to, etc.) by associating the content of recommended items or the opinions of other individuals with the consuming user’s actions or opinions. Such systems have become powerful tools in domains from electronic commerce to digital libraries and knowledge management. For example, a consumer of just about any major online retailer who expresses an interest in an item – either through viewing a product description or by placing the item in his “shopping cart” – will likely receive recommendations for additional products. These products can be recommended based on the top overall sellers on a site, on the demographics of the consumer, or on an analysis of the past buying behavior of the consumer as a prediction for future buying behavior. This paper will address the technology used to generate rec...

… Systems & the …, 2009

The Social Web has been enjoying huge popularity in recent years, attracting millions of visitors on sites such as Facebook, Delicious or YouTube. Today, we are no longer mere consumers of information, but we also actively participate in social networks, upload our personal photos, share our bookmarks, write web logs and annotate and comment on the information provided by others. Following the exponential growth in the popularity of Social Web sites, many traditional, non-social sites, are now implementing social features. Likewise many enterprises are deploying internal social media sites to support expertise location and sharing of work-related information and knowledge. The Social Web therefore provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.  New application areas for recommender systems emerge with the popularity of the Social Web. Recommenders can not only be used to sort and filter Web 2.0 and social network information, they can also support users in the information sharing process, e.g., by recommending suitable tags during folksonomy development.  Social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. This social layer can also be used as evidence on which to infer relationships and trust levels between users for recommendation generation.  The Social Web also presents new challenges for recommender systems, such as the complicated nature of human-to-human interaction which comes into play when recommending people. Or, the design and development of more interactive and richer recommender system user interfaces that enable users to express their opinions and preferences in an intuitive and effortless manner.

Proceedings of the …, 2007

IEEE/WIC/ACM International Conference on Web Intelligence

Big data are everywhere. World Wide Web and social networks are examples of these big data. They have become a vast data production and consumption platform, at which threads of data evolve from multiple devices, by different human interactions, over worldwide locations, under divergent distributed settings. Embedded in these big web data is implicit, previously unknown and potentially useful information and knowledge that awaited to be discovered. This calls for web intelligence solutions, which make good use of data science, data mining (especially, web mining) and social network analysis to discover useful knowledge and important information from the web (e.g., web of people/things). Such a web often consists of vertices (i.e., people/things) and edges (i.e., connections among people/things). When modeling a social network as a web of people, these edges can be undirected (e.g., for mutual friendships) or directed (e.g., for capturing a social entity who is following another social entity). In this paper, we present a web intelligence solution to discover interesting knowledge (e.g., most-followed people or most-referenced web pages) from these social connections. Due to the dynamic nature of the web, vertices and/or edges may be changed (e.g., added or deleted) over time. Hence, our solution is designed in such a way that it discovers knowledge not only from a static web but also from a dynamic web. Evaluation on real-life web data demonstrates the effectiveness and practicality of our solution for discovering knowledge and supporting recommendations from the web.

Foundations of Computing and Decision Sciences

In the era of internet access, recommender systems try to alleviate the difficulty consumers face while trying to find items (e.g. services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of content-based recommender systems, and not only in one part of it. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a content-based recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this resea...

2020

Recommender systems help users to reduce the information overload they may suffer in the current era of Big Data, by offering them recommendations of relevant items according to their tastes/preferences and/or context (location, weather, time of the day, etc.). We argue that text mining techniques can be exploited for the development of recommender systems. Thus, they can be applied to detect user preferences (user profiling) and also to extract context data. For this purpose, text mining can be applied on user reviews, text descriptions associated to the items, and other texts written by the user (e.g., posts in social networks). In this paper, we provide an overview of works exploiting text mining techniques in the field of recommender systems, characterizing them according to their purpose and the type of textual data analyzed.

Recommender Systems Handbook, 2015

Content-based recommender systems (CBRS) filter very large repositories of items (books, news, music tracks, TV assets, web pages?) by analyzing items previously rated by a user and building a model of user interests, called user profile, based on the features of the items rated by that user. The user profile is then exploited to recommend new potentially relevant items.

The explosive growth and availability of data on the internet has caused information overload. Searching for a query is not easy in the sources of information available for the interest of an individual user. Collaborative filtering systems recommend items based upon opinions of people with similar tastes. Collaborative filtering overcomes some difficulties faced by traditional information filtering by eliminating the need for computers to understand the content of the items. Further, collaborative filtering can also recommend articles that are not similar in content to items rated in the past as long as like-minded users have rated the items. Collaborative filtering (CF) is one of the most frequently used techniques in personalized recommendation systems. But currently used CF techniques are based on item rating prediction. We proposed an improved personalized recommended CF algorithm. Hybrid recommender systems or content-boosted technologies are quickly produce high quality recommendations. We have explored content-boosted CF technique which analyzes the user-item matrix to identify relationships between different items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze different Memorybased CF and Model-based CF techniques. Finally, we experimentally evaluate our results and compare them. The testing results show that in most cases, the improved algorithm that we put forward can improve recommendation quality.