Perceiving the next choice with comprehensive transaction embeddings for online recommendation

Shoujin Wang, Liang Hu, Longbing Cao

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

6 Citations (Scopus)

Abstract

To predict customer’s next choice in the context of what he/she has bought in a session is interesting and critical in the transaction domain especially for online shopping. Precise prediction leads to high quality recommendations and thus high benefit. Such kind of recommendation is usually formalized as transaction-based recommender systems (TBRS). Existing TBRS either tend to recommend popular items while ignore infrequent and newly-released ones (e.g., pattern-based RS) or assume a rigid order between items within a transaction (e.g., Markov Chain-based RS) which does not satisfy real-world cases in most time. In this paper, we propose a neural network-based comprehensive transaction embedding model (NTEM) which can effectively perceive the next choice in a transaction context. Specifically, we learn these comprehensive embeddings of both items and their features from relaxed ordered transactions. The relevance between items revealed by the transactions is encoded into such embeddings. With rich information embedded, such embeddings are powerful to predict the next choices given those already bought items. NTEM is a shallow wide-in-wide-out network, which is more efficient than deep networks considering large numbers of items and transactions. Experimental results on real-world datasets show that NTEM outperforms three typical TBRS models FPMC, PRME and GRU4Rec in terms of recommendation accuracy and novelty. Our implementation is available at https://github.com/shoujin88/NTEM-model.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2017 Proceedings, Part II
EditorsMichelangelo Ceci, Jaakko Hollmén, Ljupčo Todorovski, Celine Vens, Sašo Džeroski
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages285-302
Number of pages18
ISBN (Electronic)9783319712468
ISBN (Print)9783319712451
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2017 - Skopje, Macedonia, The Former Yugoslav Republic of
Duration: 18 Sep 201722 Sep 2017

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume10535
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2017
CountryMacedonia, The Former Yugoslav Republic of
CitySkopje
Period18/09/1722/09/17

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  • Cite this

    Wang, S., Hu, L., & Cao, L. (2017). Perceiving the next choice with comprehensive transaction embeddings for online recommendation. In M. Ceci, J. Hollmén, L. Todorovski, C. Vens, & S. Džeroski (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017 Proceedings, Part II (pp. 285-302). (Lecture Notes in Artificial Intelligence; Vol. 10535). Cham: Springer, Springer Nature. https://doi.org/10.1007/978-3-319-71246-8_18