Adaptive web search based on user profile constructed without any effort from users

By Kazunari Sugiyama, Kenji Hatano, Masatoshi Yoshikawa. 2004

852 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems

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In Computer Vision and Pattern Recognition

In Information Systems

Citations in the corpus

From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews (2013, background): “As discussed in [20], few previous works had dealt with temporal dynamics, other than a few notable exceptions [2, 10, 35].”
Modeling the impact of short- and long-term behavior on search personalization (2012, background): “Earlier attempts to address this challenge leveraged different representations for each source [18] or made ad hoc decisions around how to weight distant actions [27].”
A personalized search engine based on Web‐snippet hierarchical clustering (2005, background): “In addition, we mention that several other methodologies have been proposed to implicitly track user activities for enhanced Web searching, such as users query history [57], browsing history [58,59], Web communities [54,59,60] and rich client-side interactions [58,61].”
User Profiles for Personalized Information Access (2007, background): “However, the results of experiments on fully automatic profile updating are promising [11, 12, 18, 72, 93].”
Collaborative filtering with temporal dynamics (2010, background): “Nonetheless, with very few exceptions (e.g., Ding and Li, and Sugiyama et al. 4, 16 ), the recommenders’ literature does not address temporal changes in user behavior.”
Mining long-term search history to improve search accuracy (2006, background): “Such work includes [9], which interpolates the current query with different chunks (time periods) of history (browsed web pages) for personalized search, and [10], which constructs user profiles from indexed desktop documents for search result reranking.”
Personalizing search via automated analysis of interests and activities (2005, background): “A wide range of implicit user activities have been proposed as sources of information for enhanced Web search, including the user’s query history [20][22], browsing history [16][23], Web communities [13][ 23 ], and rich client-side interactions [3][4][16].”
A large-scale evaluation and analysis of personalized search strategies (2007, methodology): “The typical evaluation method used in existing personalized search research is to conduct user studies [23, 26, 6, 34, 28, 29, 19, 5, 31].”
Collaborative filtering with temporal dynamics (2009, background): “[17] proposed a personalized web search engine, where they let the user profile evolve over time.”