An Efficient Greedy Algorithm for Sequence Recommendation

Mai Thai Son, Ira Assent, Mathias Skovgaard Birk, Martin Storgaard Dieu, Jon Jacobsen, Jesper Kristensen, Habibur Rahman, Saravanan Thirumuruganathan, Gautam Das, Behrooz Omidvar-Tehrani, Ria Mae Borromeo, Lei Chen, Renée Miller, Idir Benouaret, Sihem Amer-Yahia, Senjuti Basu Roy

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Abstract

Recommending a sequence of items that maximizes some objective function arises in many real-world applications. In this paper, we consider a utility function over sequences of items where sequential dependencies between items are modeled using a directed graph. We propose EdGe, an efficient greedy algorithm for this problem and we demonstrate its effectiveness on both synthetic and real datasets. We show that EdGe achieves comparable recommendation precision to the state-of-the-art related work OMEGA, and in considerably less time. This work opens several new directions that we discuss at the end of the paper.

Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications - 30th International Conference, DEXA 2019, Proceedings
Number of pages13
Publication date2019
Pages314-326
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • Algorithms
  • Sequence recommendation
  • Submodular maximization

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