Technical Paper Recommendation: A Study in Combining Multiple Information Sources

TL;DR

This paper explores combining multiple information sources for technical paper recommendation, focusing on recommending conference submissions to reviewers, using information retrieval techniques and novel interest data collection methods.

Contribution

It introduces a system integrating multiple data sources for paper recommendation and evaluates its effectiveness against existing methods.

Findings

Effective recommendation performance demonstrated

Novel interest data collection from the Web

Comparison with other methods shows improved results

Abstract

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.