An Efficient Greedy Algorithm for Sequence Recommendation
Document Type
Conference Proceeding
Publication Date
1-1-2019
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.
Identifier
85077111999 (Scopus)
ISBN
[9783030276140]
Publication Title
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
External Full Text Location
https://doi.org/10.1007/978-3-030-27615-7_24
e-ISSN
16113349
ISSN
03029743
First Page
314
Last Page
326
Volume
11706 LNCS
Recommended Citation
Benouaret, Idir; Amer-Yahia, Sihem; and Roy, Senjuti Basu, "An Efficient Greedy Algorithm for Sequence Recommendation" (2019). Faculty Publications. 7993.
https://digitalcommons.njit.edu/fac_pubs/7993
