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Book ChapterDOI

A Framework to Infer Webpage Relevancy for a User

TL;DR: This work aims to create user profiles automatically and implicitly depending on the various web pages a user browses over a period of time and the user's interaction with them, which indicates relevancy of web pages to the user based on these weights.
Abstract: The Web is a vast pool of resources which comprises of a lot of web pages covering all aspects of life. Understanding a user’s interests is one of the major research areas towards understanding the web today. Identifying the relevance of the surfed web pages for the user is a tedious job. Many systems and approaches have been proposed in literature, to try and get information about the user’s interests by user profiling. This paper proposes an improvement in determining the relevance of the webpage to the user, which is an extension to the relevance formula that was proposed earlier. The current work aims to create user profiles automatically and implicitly depending on the various web pages a user browses over a period of time and the user’s interaction with them. This automatically generated user profile assigns weights to web pages proportional to the user interactions on the webpage and thus indicates relevancy of web pages to the user based on these weights.
Citations
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Journal ArticleDOI
TL;DR: In this article , the authors aim to bank upon the state-of-the-art literature that has been written previously in this regard and derive though provoking research questions, along with few recommendations that can be viably used in developing frameworks and mechanisms for the synergic co-existence of these two challenging, yet interesting fields of AI and HCI.

2 citations

Journal ArticleDOI
TL;DR: In this paper , a comparative analysis of machine learning algorithms for exoplanet detection is presented, which identifies the pros and cons of different algorithms for analysing certain forms of data.
References
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Proceedings ArticleDOI
15 Aug 2005
TL;DR: This research suggests that rich representations of the user and the corpus are important for personalization, but that it is possible to approximate these representations and provide efficient client-side algorithms for personalizing search.
Abstract: We formulate and study search algorithms that consider a user's prior interactions with a wide variety of content to personalize that user's current Web search. Rather than relying on the unrealistic assumption that people will precisely specify their intent when searching, we pursue techniques that leverage implicit information about the user's interests. This information is used to re-rank Web search results within a relevance feedback framework. We explore rich models of user interests, built from both search-related information, such as previously issued queries and previously visited Web pages, and other information about the user such as documents and email the user has read and created. Our research suggests that rich representations of the user and the corpus are important for personalization, but that it is possible to approximate these representations and provide efficient client-side algorithms for personalizing search. We show that such personalization algorithms can significantly improve on current Web search.

928 citations

Book ChapterDOI
25 Mar 2002
TL;DR: The research focuses on the degree to which implicit evidence of document relevance can be substituted for explicit evidence in terms of both user opinion and search effectiveness.
Abstract: In this paper we report on the application of two contrasting types of relevance feedback for web retrieval. We compare two systems; one using explicit relevance feedback (where searchers explicitly have to mark documents relevant) and one using implicit relevance feedback (where the system endeavours to estimate relevance by mining the searcher's interaction). The feedback is used to update the display according to the user's interaction. Our research focuses on the degree to which implicit evidence of document relevance can be substituted for explicit evidence. We examine the two variations in terms of both user opinion and search effectiveness.

131 citations

Proceedings ArticleDOI
23 Apr 2006
TL;DR: A large-scale user study on which users' searches were observed by a specially developed browser that recorded their behavior as well as their explicit ratings found a certain combination of implicit indicators achieved higher correlation with the explicit ratings than any of the individual indicators.
Abstract: Explicit relevance feedback involves explicit ratings of documents or terms by users and disrupts their browsing and searching. The alternative non-disruptive method is implicit feedback inferring users' needs and interests by monitoring their regular interaction with the system. Some implicit indicators of interest, such as reading time, have been investigated in previous studies and were found indicative to the relevance of documents but not sufficiently accurate [1,2,3,4]. In this paper we present and examine several new relative implicit feedback indicators, and study the effect of combining several implicit indicators. The paper describes a large-scale user study on which users' searches were observed by a specially developed browser that recorded their behavior (implicit indicators) as well as their explicit ratings. We analyzed the relationship between implicit indicators and explicit ratings and found that a certain combination of implicit indicators achieved higher correlation with the explicit ratings than any of the individual indicators. We have also found that the relative indicators are more indicative to the level of interest of a user item than the non-relative indicators.

52 citations

Proceedings ArticleDOI
11 Jul 2011
TL;DR: This paper provides a detailed description of an algorithm developed to predict which paragraphs of text in a hypertext document have been read, and to which extent, and describes the user study that served as the basis for the algorithm.
Abstract: The main source of information in most adaptive hypermedia systems are server monitored events such as page visits and link selections. One drawback of this approach is that pages are treated as "monolithic" entities, since the system cannot determine what portions may have drawn the user's attention. Departing from this model, the work described here demonstrates that client-side monitoring and interpretation of users' interactive behavior (such as mouse moves, clicks and scrolling) allows for detailed and significantly accurate predictions on what sections of a page have been looked at. More specifically, this paper provides a detailed description of an algorithm developed to predict which paragraphs of text in a hypertext document have been read, and to which extent. It also describes the user study, involving eye-tracking for baseline comparison, that served as the basis for the algorithm.

36 citations

Proceedings ArticleDOI
27 Oct 2014
TL;DR: This paper summarizes work done in the area of identifying user's interests implicitly through the actions performed by the user through his browser, while using the web and proposes measures to improve the efficiency of such systems by using a combinatorial approach.
Abstract: In recent years, there has been a tremendous growth in the web and its usage, so much so that today many users find it difficult to get information that is relevant to them. This implies that there is a need to prioritize and try to get the information which the user is interested in. The behavior of the user is dynamic which makes it difficult to track his current interests and changes in his interests. If the user's interests are asked explicitly, most users tend to either ignore giving information or fill in wrong/incomplete information. Implicit user interested identification thus becomes imperative, which will not hamper his day-to-day web usage but the system learns about the users interests automatically, unobtrusively. This paper summarizes work done in the area of identifying user's interests implicitly through the actions performed by the user through his browser, while using the web. It also proposes measures to improve the efficiency of such systems by using a combinatorial approach.

11 citations