Information Filtering: Overview of Issues, Research and Systems

By U. Hanani, Bracha Shapira, P. Shoval. User modeling and user-adapted interaction, 2001

462 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1abfc42efe166b7b08e298f34384f179b85bc894

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In Information Systems

Citations in the corpus

Solving the apparent diversity-accuracy dilemma of recommender systems (2008, background): “Whereas information filtering tools like search engines typically require the user to specify in advance what they are looking for [2, 3, 4, 5], this challenge of identifying user needs is the domain of recommender systems [5, 6, 7, 8], which attempt to anticipate future likes and interests by minin”
Adaptive Instructional Systems (2007, unknown): “information relevant only to them (Hanani et al., 2001).”
E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact (2007, background): “…shopping history, or choices made by other consumers with similar profiles, RAs have the potential to reduce consumers’ information overload and search complexity, while at the same time improving their decision quality (Chiasson et al. 2002; Hanani et al. 2001; Haubl and Trifts 2000; Maes 1994).”
Adaptive interfaces and agents (2002, background): “Figure 15.12 shows part of a screen from the tourist information system AVANTI 2Surveys of parts of this large area are provided by, among others, Brusilovsky (1996, 2001), Hanani, Shapira, and Shoval (2001), and Mladenic (1999).”
Challenges and Solutions of Ubiquitous User Modeling (2012, methodology): “of them, as described below, following [Hanani et al., 2001].”
Efﬁcient Filtering on Hidden Document Streams (unknown): “Information Filtering The motivation of our work is similar to the classical Personalized Information Filtering Problem (Hanani, Shapira, and Shoval 2001; Belkin and Croft 1992; Foltz and Dumais 1992).”
Efficient Filtering on Hidden Document Streams (2014, methodology): “Information Filtering The motivation of our work is similar to the classical Personalized Information Filtering Problem (Hanani, Shapira, and Shoval 2001; Belkin and Croft 1992; Foltz and Dumais 1992).”
Query Expansion Techniques for Information Retrieval: a Survey (2017, unknown): “Reference [108] gives a brief overview of IF and discuss the difference between IR and IF with respect to research issues.”