Semantics and Agents Oriented Web Personalization

Semantics, Web and …, 2006

Web personalization is the process of customizing a web site to the needs of each specific user or set of users. Personalization of a web site may be performed by the provision of recommendations to the users, highlighting/adding links, creation of index pages, etc. The web personalization systems are mainly based on the exploitation of the navigational patterns of the web site's visitors. When a personalization system relies solely on usage-based results, however, valuable information conceptually related to what is finally recommended may be missed. The exploitation of the web pages' semantics can considerably improve the results of web usage mining and personalization, since it provides a more abstract yet uniform and both machine and human understandable way of processing and analyzing the usage data. The underlying idea is to integrate usage data with content semantics, expressed in ontology terms, in order to produce semantically enhanced navigational patterns that can subsequently be used for producing valuable recommendations. In this paper we propose a semantic web personalization system, focusing on word sense disambiguation techniques which can be applied in order to semantically annotate the web site's content.

Over the last decade the data on World Wide Web has been growing in an exponential manner. According to Google the data is accelerating with a speed of billion pages per day [24]. Internet has around 2 million users accessing the World Wide Web for various information [25].These numbers certainly raise a severe concern over information over load challenges for the users. Many researchers have been working to overcome the challenge with web personalization, many researchers are looking at ontology based web personalization as an answer to the information overload, as each individual is unique. In this paper we present an overview of ontology based web personalization, Challenges and a survey of the work. This paper also points future work in web personalization.

This paper introduces a new model of personalized usage of the internet that is based on technologies of user representation, artificial intelligence and semantic augmentation of the content. By taking advantage of internet's unprecedented dynamics, compared to traditional media, this user representation model incorporates cognitive, mainly, psychology theories, combined with parameters that constitute more traditional approaches in user profiling (such as demographics, expertise, etc). The purpose of this research is to alleviate difficulties that massive approaches impose on areas such as education and information processing, by integrating intelligent adaptive characteristics into web applications; this can lead to a highly adapted to each user's needs content and more effective, in our case, learning.

ACM Transactions on Internet Technology, 2007

In this chapter we provide a comprehensive overview of the topic of Intelligent Techniques for Web Personalization. Web Personalization is viewed as an application of data mining and machine learning techniques to build models of user behaviour that can be applied to the task of predicting user needs and adapting future interactions with the ultimate goal of improved user satisfaction. This chapter survey's the state-of-the-art in Web personalization. We start by providing a description of the personalization process and a classification of the current approaches to Web personalization. We discuss the various sources of data available to personalization systems, the modelling approaches employed and the current approaches to evaluating these systems. A number of challenges faced by researchers developing these systems are described as are solutions to these challenges proposed in literature. The chapter concludes with a discussion on the open challenges that must be addressed by the research community if this technology is to make a positive impact on user satisfaction with the Web.

2004

Currently, adaptive systems are described with non-uniform methods, depending on the specific view on the system, the application, or other parameters. There is no common language for expressing adaptive functionality, hence these systems are difficult to compare and analyse, and lack of re-usability which in fact is the key-capability for successfull personalization functionality for the Semantic Web. In the deliverables on adaptive functionality we aim at developing a logical description of adaptive functionality. Work in month 1–6 has concentrated on identifing some personalization techniques that are appropriate candidates for personalization on the Semantic Web, to provide a collection of detailed descriptions of identified personalization functionality, and to outline demands for architectures, supporting techniques, and rule and reasoning languages for the Semantic Web. Keyword List personalization, adaptive hypermedia, recommendation systems, artificial immune systems, seman...

1997

Agents can personalize otherwise impersonal computational systems. The World Wide Web presents the same appearance to every user regardless of that user’s past activity. Web Browser Intelligence (WBI, pronounced “WEB-ee”) is an implemented system that organizes agents on a user’s workstation to observe user actions, proactively offer assistance, modify web documents, and perform new functions. WBI can annotate hyperlinks with network speed information, record pages viewed for later access, and provide shortcut links for common paths. In this way, WBI personalizes a user’s web experience by joining personal information with global information to effectively tailor what the user sees.

2008

Web browsing is a complex activity and in general, users are not guided during browsing. Our hypothesis is that by using Semantic Web technologies and personalization methods, browsing can be supported better. However, existing personalization mechanisms on the Web are obstructive; users need to log in to multiple websites and enter their personal information and preferences, and the profiles are different for each site. There is a need for generic user profiles, which can also support the user's browsing.

Egyptian Informatics Journal, 2012

The World Wide Web has become the largest library through the history of the humanity. Having such a huge library made the search process more complex as the syntactic search engines offer an overwhelming amount of search results. Vocabulary problems like polysemy and synonymy can make the search results of traditional search engines irrelevant to users. Such problems trigger a strong need for personalizing the web search results based on user preferences. In this paper, we propose a new multi-agent system based approach for personalizing the web search results. The proposed approach introduces a model to build a user profile from initial and basic information, and maintain it through implicit user feedback to establish a complete, dynamic and up-to-date user profile. In the web search process, the model semantically optimizes the user query in two steps: query optimization using user profile preferences and query optimization using the WordNet ontology. The model builds on the advantages of the current search engines by utilizing them for retrieving the web search results. We present a detailed case study and simulation results evaluation to illustrate how the proposed model works and its expected value in increasing the precision of the traditional search engines and solving the vocabulary problems.

1999

In the SmartPush project professional editors add semantic metadata to information flow when the content is created. This metadata is used to filter the information flow to provide the end users with a personalized news service. Personalization and delivery process is modeled as software agents, to whom the user delegates the task of sifting through incoming information. The key components of the SmartPush architecture have been implemented, and the focus in the project is shifting towards a pilot implementation and testing the ideas in practice.

Citeseer

Nowadays, users are exposed to an overwhelming amount of information in several application domains. Recommender systems fight such an overload by selecting the products which are more appealing to each user, according to his personal preferences or needs. Current personalization techniques are based on more or less sophisticated syntactic methods which miss a lot of knowledge during the elaboration of the recommendations. In this paper, we propose an approach that effectively overcomes the drawbacks of the existing personalization techniques by resorting to reasoning mechanisms inspired in Semantic Web technologies. Such a reasoning provides recommender system with extra knowledge about the user's preferences, thus favoring more accurate personalization processes.