User Profiles for Personalized Information Access

By Susan Gauch, Mirco Speretta, Aravind Chandramouli, Alessandro Micarelli. The Adaptive Web, 2007

617 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/0d6b69f39896b7216b2ee68f2a813b5068d3a67c

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In Computer Networks and Communications

In Information Systems

Citations in the corpus

Learning under Concept Drift: an Overview (2010, background): “[1 57 ] pr ed .”
User Models for Adaptive Hypermedia and Adaptive Educational Systems (2007, neutral): “The review of the structure and the representation of information in user models (the second layer) is split between this chapter and Chapter 2 [72].”
Personalized Search (2015, background): “In order to address the issue of irrelevant ranking, personalized search has attracted much interest in both academia [16] [46] [45] [25] [43] [35] [4] [3] and in the enterprise world, for instance at Amazon [28], Netflix [2], Facebook [48], Yahoo! [38], Microsoft [42], and Google [6] [11].”
A case study of behavior-driven conjoint analysis on Yahoo!: front page today module (2009, background): “[8] gave an extensive review on various profiling techniques.”
Personalized recommendation on dynamic content using predictive bilinear models (2009, methodology): “A review of various user profiling techniques is provided in [19].”
SCENE: a scalable two-stage personalized news recommendation system (2011, background): “A comprehensive survey of various user profile construction techniques is provided in [9].”
Data Mining for Web Personalization (2007, background): “For a detailed discussion of various approaches to Web user proﬁling see Chapter 2 of this book [40].”
Like like alike — Joint Friendship and Interest Propagation in Social Networks Shuang (2011, background): “Instead they either employ feature engineering to generate hand-crafted meta-descriptors as fingerprint for a user [26] [5] or they extract a set of latent features by factorizing a user's registered profile data; for example, by means of sparse coding [12] or latent Dirichlet allocation [2].”
Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis (2015, neutral): “A review of various user/item profiling techniques is provided in [14].”
Bias in algorithmic filtering and personalization (2013, background): “It is also known that users may not accurately report their own interests or demographic data, or, since the profile remains static whereas the user's interests may change over time (Gauch et al. 2007).”