Inproceedings,

Automatically building research reading lists

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Proceedings of the fourth ACM conference on Recommender systems, page 159--166. New York, NY, USA, ACM, (2010)
DOI: 10.1145/1864708.1864740

Abstract

All new researchers face the daunting task of familiarizing themselves with the existing body of research literature in their respective fields. Recommender algorithms could aid in preparing these lists, but most current algorithms do not understand how to rate the importance of a paper within the literature, which might limit their effectiveness in this domain. We explore several methods for augmenting existing collaborative and content-based filtering algorithms with measures of the influence of a paper within the web of citations. We measure influence using well-known algorithms, such as HITS and PageRank, for measuring a node's importance in a graph. Among these augmentation methods is a novel method for using importance scores to influence collaborative filtering. We present a task-centered evaluation, including both an offline analysis and a user study, of the performance of the algorithms. Results from these studies indicate that collaborative filtering outperforms content-based approaches for generating introductory reading lists.

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  • @jaeschke
    @jaeschke 13 years ago
    Given a list of relevant papers for a certain topic, the goal is to get further important and relevant papers for that topic. This commonly happens when one enters a new field of research. The authors tackle the problem by combining Collaborative Filtering with graph-based approaches like PageRank and HITS. Overall, they evaluate over 177 variants of combinations on a dataset from the ACM digital library and the three best approaches in a user study. The citation graph is used as input for the graph ranking algorithms. No social data is incorporated.
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