Content-boosted collaborative filtering for improved recommendations

Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, <i>Content-Boosted Collaborative Filtering</i>, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[3]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[4]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

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

[6]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[7]  John Riedl,et al.  Combining Collaborative Filtering with Personal Agents for Better Recommendations , 1999, AAAI/IAAI.

[8]  Mark Claypool,et al.  Combining Content-Based and Collaborative Filters in an Online Newspaper , 1999, SIGIR 1999.

[9]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[10]  Ian Soboroff. Charles Nicholas.  Combining Content and Collaboration in Text Filtering , 1999 .

[11]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[12]  Mark W. Newman,et al.  SWAMI: a framework for collaborative filtering algorithm development and evaluation. , 2000, SIGIR 2000.

[13]  Richard W. Vuduc,et al.  SWAMI (poster session): a framework for collaborative filtering algorithm development and evaluation , 2000, SIGIR '00.

[14]  Barry Smyth,et al.  PTV: Intelligent Personalised TV Guides , 2000, AAAI/IAAI.

[15]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[16]  Wee Sun Lee.  Collaborative Learning for Recommender Systems , 2001 .

[17]  Wee Sun Lee.  Collaborative Learning and Recommender Systems , 2001, ICML.

[18]  David M. Pennock,et al.  Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments , 2001, UAI.

[19]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.