An Efficient Greedy Algorithm for Sequence Recommendation

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.

[1]  Jianyong Wang,et al.  Mining sequential patterns by pattern-growth: the PrefixSpan approach , 2004, IEEE Transactions on Knowledge and Data Engineering.

[2]  Laks V. S. Lakshmanan,et al.  Breaking out of the box of recommendations: from items to packages , 2010, RecSys '10.

[3]  Jie Xu,et al.  Personalized Course Sequence Recommendations , 2015, IEEE Transactions on Signal Processing.

[4]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[5]  Mohammed J. Zaki,et al.  SPADE: An Efficient Algorithm for Mining Frequent Sequences , 2004, Machine Learning.

[6]  G. James Blaine,et al.  Continuous Monitoring of Physiologic Variables with a Dedicated Minicomputer , 1975, Computer.

[7]  Andreas Krause,et al.  Submodular Function Maximization , 2014, Tractability.

[8]  Andreas Krause,et al.  Selecting Sequences of Items via Submodular Maximization , 2017, AAAI.

[9]  Daqing Zhang,et al.  Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[10]  Sihem Amer-Yahia,et al.  Composite Retrieval of Diverse and Complementary Bundles , 2014, IEEE Transactions on Knowledge and Data Engineering.

[11]  Cong Yu,et al.  Interactive itinerary planning , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[12]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[13]  Andreas Krause,et al.  Differentiable Submodular Maximization , 2018, IJCAI.

[14]  Mohamed Lazaar,et al.  Collaborative Filtering Recommender System , 2018, Advances in Intelligent Systems and Computing.

[15]  Feng Yu,et al.  A Dynamic Recurrent Model for Next Basket Recommendation , 2016, SIGIR.

[16]  Aditya G. Parameswaran,et al.  Recommendation systems with complex constraints: A course recommendation perspective , 2011, TOIS.

[17]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[18]  Robin Burke,et al.  Context-aware music recommendation based on latenttopic sequential patterns , 2012, RecSys.

[19]  Lars Schmidt-Thieme,et al.  Factorizing personalized Markov chains for next-basket recommendation , 2010, WWW '10.

[20]  Lior Rokach,et al.  Recommender Systems: Introduction and Challenges , 2015, Recommender Systems Handbook.

[21]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[22]  Wolfgang Wörndl,et al.  Recommending a sequence of interesting places for tourist trips , 2017, J. Inf. Technol. Tour..

[23]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.