An Efficient Greedy Algorithm for Sequence Recommendation

Idir Benouaret, Sihem Amer-Yahia, Senjuti Basu Roy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


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 (US)
Title of host publicationDatabase and Expert Systems Applications - 30th International Conference, DEXA 2019, Proceedings
EditorsSven Hartmann, Josef Küng, Gabriele Anderst-Kotsis, Ismail Khalil, Sharma Chakravarthy, A Min Tjoa
Number of pages13
ISBN (Print)9783030276140
StatePublished - 2019
Event30th International Conference on Database and Expert Systems Applications, DEXA 2019 - Linz, Austria
Duration: Aug 26 2019Aug 29 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11706 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference30th International Conference on Database and Expert Systems Applications, DEXA 2019

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Algorithms
  • Sequence recommendation
  • Submodular maximization


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