Implicit feedback for inferring user preference: a bibliography

Relevance feedback has a history in information retrieval that dates back well over thirty years (c.f [SL96]). Relevance feedback is typically used for query expansion during short-term modeling of a user's immediate information need and for user profiling during long-term modeling of a user's persistent interests and preferences. Traditional relevance feedback methods require that users explicitly give feedback by, for example, specifying keywords, selecting and marking documents, or answering questions about their interests . Such relevance feedback methods force users to engage in additional activities beyond their normal searching behavior . Since the cost to the user is high and the benefits are not always apparent, it can be difficult to collect the necessary data and the effectiveness of explicit techniques can be limited.

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[17]  Bill N. Schilit,et al.  From reading to retrieval: freeform ink annotations as queries , 1999, SIGIR '99.

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[19]  Michael J. Pazzani,et al.  A personal news agent that talks, learns and explains , 1999, AGENTS '99.

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[22]  Jon Kleinberg,et al.  Authoritative sources in a hyperlinked environment , 1999, SODA '98.

[23]  Douglas W. Oard,et al.  Using Implicit Feedback for User Modeling in Internet and Intranet Searching ϕ , 2000 .

[24]  Bradley J. Rhodes,et al.  Margin notes: building a contextually aware associative memory , 2000, IUI '00.

[25]  C. Lee Giles,et al.  Discovering Relevant Scientific Literature on the Web , 2000, IEEE Intell. Syst..

[26]  Paul P. Maglio,et al.  SUITOR: an attentive information system , 2000, IUI '00.

[27]  Michael J. Pazzani,et al.  A learning agent for wireless news access , 2000, IUI '00.

[28]  Young-Woo Seo,et al.  A reinforcement learning agent for personalized information filtering , 2000, IUI '00.

[29]  Michael D. Cooper,et al.  Predicting the relevance of a library catalog search , 2001, J. Assoc. Inf. Sci. Technol..

[30]  Barry Smyth,et al.  Passive Profiling from Server Logs in an Online Recruitment Environment , 2001, IJCAI 2001.

[31]  Douglas W. Oard,et al.  Modeling Information Content Using Observable Behavior , 2001 .

[32]  Mark Claypool,et al.  Implicit interest indicators , 2001, IUI '01.

[33]  Nicholas J. Belkin,et al.  Reading time, scrolling and interaction: exploring implicit sources of user preferences for relevance feedback , 2001, Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.

[34]  David M. Pennock,et al.  REFEREE: An Open Framework for Practical Testing of Recommender Systems using ResearchIndex , 2002, VLDB.

[35]  Clement T. Yu,et al.  Personalized web search by mapping user queries to categories , 2002, CIKM '02.

[36]  Diane Kelly,et al.  The effects of topic familiarity on information search behavior , 2002, JCDL '02.

[37]  Ryen W. White,et al.  Finding relevant documents using top ranking sentences: an evaluation of two alternative schemes , 2002, SIGIR '02.