Proceedings ArticleDOI
Implicit interest indicators
Mark Claypool,Phong Le,Makoto Wased,David C. Brown +3 more
- pp 33-40
TLDR
It was found that the time spent on a pages, the amount of scrolling on a page and the combination of time and scrolling had a strong correlation with explicit interest, while individual scrolling methods and mouse-clicks were ineffective in predicting explicit interest.Abstract:
Recommender systems provide personalized suggestions about items that users will find interesting. Typically, recommender systems require a user interface that can ``intelligently'' determine the interest of a user and use this information to make suggestions. The common solution, ``explicit ratings'', where users tell the system what they think about a piece of information, is well-understood and fairly precise. However, having to stop to enter explicit ratings can alter normal patterns of browsing and reading. A more ``intelligent'' method is to useimplicit ratings, where a rating is obtained by a method other than obtaining it directly from the user. These implicit interest indicators have obvious advantages, including removing the cost of the user rating, and that every user interaction with the system can contribute to an implicit rating.Current recommender systems mostly do not use implicit ratings, nor is the ability of implicit ratings to predict actual user interest well-understood. This research studies the correlation between various implicit ratings and the explicit rating for a single Web page. A Web browser was developed to record the user's actions (implicit ratings) and the explicit rating of a page. Actions included mouse clicks, mouse movement, scrolling and elapsed time. This browser was used by over 80 people that browsed more than 2500 Web pages.Using the data collected by the browser, the individual implicit ratings and some combinations of implicit ratings were analyzed and compared with the explicit rating. We found that the time spent on a page, the amount of scrolling on a page and the combination of time and scrolling had a strong correlation with explicit interest, while individual scrolling methods and mouse-clicks were ineffective in predicting explicit interest.read more
Citations
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Proceedings ArticleDOI
Collaborative topic modeling for recommending scientific articles
Chong Wang,David M. Blei +1 more
TL;DR: An algorithm to recommend scientific articles to users of an online community that combines the merits of traditional collaborative filtering and probabilistic topic modeling and can form recommendations about both existing and newly published articles is developed.
Proceedings ArticleDOI
Accurately interpreting clickthrough data as implicit feedback
TL;DR: It is concluded that clicks are informative but biased, and while this makes the interpretation of clicks as absolute relevance judgments difficult, it is shown that relative preferences derived from clicks are reasonably accurate on average.
Journal ArticleDOI
Collaborative filtering recommender systems
Mehrbakhsh Nilashi,Karamollah Bagherifard,Othman Ibrahim,Hamid Alizadeh,Lasisi Ayodele Nojeem,Nazanin Roozegar +5 more
TL;DR: This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms.
Journal ArticleDOI
Implicit feedback for inferring user preference: a bibliography
Diane Kelly,Jaime Teevan +1 more
TL;DR: Traditional relevance feedback methods require that users explicitly give feedback by specifying keywords, selecting and marking documents, or answering questions about their interests, which can be difficult to collect the necessary data and the effectiveness of explicit techniques can be limited.
Journal ArticleDOI
An MDP-Based Recommender System
TL;DR: In this paper, the authors argue that it is more appropriate to view the problem of generating recommendations as a sequential optimization problem and, consequently, that Markov decision processes (MDPs) provide a more appropriate model for recommender systems.
References
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Proceedings Article
Combining Content-Based and Collaborative Filters in an Online Newspaper
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