Recommendation : A Study in Combining Multiple Information Sources

The growing need to manage and exploit the proliferation of online data sources is opening up new opportunities for bringing people closer to the resources they need. For instance, consider a recommendation service through which researchers can receive daily pointers to journal papers in their fields of interest. We survey some of the known approaches to the problem of technical paper recommendation and ask how they can be extended to deal with multiple information sources. More specifically, we focus on a variant of this problem - recommending conference paper submissions to reviewing committee members - which offers us a testbed to try different approaches. Using WHIRL - an information integration system - we are able to implement different recommendation algorithms derived from information retrieval principles. We also use a novel autonomous procedure for gathering reviewer interest information from the Web. We evaluate our approach and compare it to other methods using preference data provided by members of the AAAI-98 conference reviewing committee along with data about the actual submissions.

[1]  Kenneth Y. Goldberg,et al.  Jester 2.0 (poster abstract): evaluation of an new linear time collaborative filtering algorithm , 1999, SIGIR '99.

[2]  Gerard Salton,et al.  Improving retrieval performance by relevance feedback , 1997, J. Am. Soc. Inf. Sci..

[3]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[4]  Martin Dillon,et al.  The Use of Automatic Relevance feedback in Boolean Retrieval Systems , 1980, J. Documentation.

[5]  Ian H. Witten,et al.  Extracting Text from PostScript , 1998, Softw. Pract. Exp..

[6]  William W. Cohen,et al.  Web-collaborative filtering: recommending music by crawling the Web , 2000, Comput. Networks.

[7]  Ian H. Witten,et al.  Extracting text from PostScript , 1998 .

[8]  Mark Rosenstein,et al.  Recommending and evaluating choices in a virtual community of use , 1995, CHI '95.

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

[10]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[11]  David Yarowsky,et al.  Taking the load off the conference chairs-towards a digital paper-routing assistant , 1999, EMNLP.

[12]  M. F. Porter,et al.  An algorithm for suffix stripping , 1997 .

[13]  Oren Etzioni,et al.  Dynamic Reference Sifting: A Case Study in the Homepage Domain , 1997, Comput. Networks.

[14]  William W. Cohen.  Integration of heterogeneous databases without common domains using queries based on textual similarity , 1998, SIGMOD '98.

[15]  Gerald Salton,et al.  Automatic text processing , 1988 .

[16]  William W. Cohen,et al.  Joins that Generalize: Text Classification Using WHIRL , 1998, KDD.

[17]  Jakob Nielsen,et al.  Automating the assignment of submitted manuscripts to reviewers , 1992, SIGIR '92.

[18]  Donna K. Harman,et al.  Relevance feedback revisited , 1992, SIGIR '92.

[19]  James Geller.  Challenge: How IJCAI 1999 can Prove Value of AI by Using AI , 1997, IJCAI.