What Happened to Content-Based Information Filtering?

Personalisation can have a significant impact on the way information is disseminated on the web today. Information Filtering can be a significant ingredient towards a personalised web. Collaborative Filtering is already being applied successfully for generating personalised recommendations of music tracks, books, movies and more. The same is not true for Content-Based Filtering. In this paper, we identify some possible reasons for the notable absence of a broad range of personalised information delivery and dissemination services on the web today. We advocate that a more holistic approach to user profiling is required and we discuss the series of still open, challenging research issues raised.

[1]  Javed Mostafa,et al.  A multilevel approach to intelligent information filtering: model, system, and evaluation , 1997, TOIS.

[2]  John Yen,et al.  Learning user interest dynamics with a three-descriptor representation , 2001 .

[3]  Victoria S. Uren,et al.  Building and applying a concept hierarchy representation of a user profile , 2003, SIGIR.

[4]  Andreas S. Weigend,et al.  Exploiting Hierarchy in Text Categorization , 1999, Information Retrieval.

[5]  Ken Lang,et al.  NewsWeeder: Learning to Filter Netnews , 1995, ICML.

[6]  Anne N. De Roeck,et al.  Multimodal Dynamic Optimization: From Evolutionary Algorithms to Artificial Immune Systems , 2007, ICARIS.

[7]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[8]  Dunja Mladenic.  Using Text Learning to help Web browsing , 2001 .

[9]  George A. Vouros,et al.  Artificial Intelligence: Theories, Models and Applications, 5th Hellenic Conference on AI, SETN 2008, Syros, Greece, October 2-4, 2008. Proceedings , 2008, SETN.

[10]  Pattie Maes,et al.  Agents that reduce work and information overload , 1994, CACM.

[11]  Manolis Vavalis,et al.  A "Bag" or a "Window" of Words for Information Filtering? , 2008, SETN.

[12]  David Buttler,et al.  Online selection of parameters in the rocchio algorithm for identifying interesting news articles , 2008, WIDM '08.

[13]  Michael J. Pazzani,et al.  Syskill & Webert: Identifying Interesting Web Sites , 1996, AAAI/IAAI, Vol. 1.

[14]  John Riedl,et al.  Recommender Systems: A GroupLens Perspective , 1998 .

[15]  Mohand Boughanem,et al.  Mercure and MercureFiltre Applied for Web and Filtering Tasks at TREC-10 , 2001, TREC.

[16]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[17]  John Yen,et al.  Learning user interest dynamics with a three-descriptor representation , 2001, J. Assoc. Inf. Sci. Technol..

[18]  Nicholas J. Belkin,et al.  Information filtering and information retrieval: two sides of the same coin? , 1992, CACM.

[19]  Anne N. De Roeck,et al.  A review of evolutionary and immune-inspired information filtering , 2010, Natural Computing.

[20]  Stefan Wermter,et al.  Neural Network Agents for Learning Semantic Text Classification , 2000, Information Retrieval.

[21]  Céline Rouveirol,et al.  Machine Learning: ECML-98 , 1998, Lecture Notes in Computer Science.

[22]  Yi Zhang.  Using bayesian priors to combine classifiers for adaptive filtering , 2004, SIGIR '04.

[23]  John Yen,et al.  An adaptive algorithm for learning changes in user interests , 1999, CIKM '99.

[24]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[25]  Gianni Amati,et al.  A Framework for Filtering News and Managing Distributed Data , 1997, J. Univers. Comput. Sci..

[26]  F HueteJuan,et al.  Combining content-based and collaborative recommendations , 2010 .

[27]  Anne N. De Roeck,et al.  Autopoiesis, the immune system, and adaptive information filtering , 2009, Natural Computing.

[28]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[29]  Beerud Dilip Sheth,et al.  A learning approach to personalized information filtering , 1994 .

[30]  Ingrid Renz,et al.  Adaptive Information Filtering: Learning in the Presence of Concept Drifts , 1998 .

[31]  Geoffrey I. Webb,et al.  # 2001 Kluwer Academic Publishers. Printed in the Netherlands. Machine Learning for User Modeling , 1999 .

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

[33]  Hector Garcia-Molina,et al.  SIFT - a Tool for Wide-Area Information Dissemination , 1995, USENIX.

[34]  Stefano Mizzaro,et al.  How many relevances in information retrieval? , 1998, Interact. Comput..

[35]  Victoria S. Uren,et al.  Immune-Inspired Adaptive Information Filtering , 2006, ICARIS.