Behavior2Vec: Generating distributed representations of users' behaviors on products for recommender systems

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20 Scopus citations

Abstract

Most studies on recommender systems target at increasing the click through rate, and hope that the number of orders will increase as well. We argue that clicking and purchasing an item are different behaviors. Thus, we should probably apply different strategies for different objectives, e.g., increase the click through rate, or increase the order rate. In this article, we propose to generate the distributed representations of users' viewing and purchasing behaviors on an e-commerce website. By leveraging on the cosine distance between the distributed representations of the behaviors on items under different contexts, we can predict a user's next clicking or purchasing item more precisely, compared to several baseline methods. Perhaps more importantly, we found that the distributed representations may help discover interesting analogies among the products. We may utilize such analogies to explain how two products are related, and eventually apply different recommendation strategies under different scenarios. We developed the Behavior2Vec library for demonstration. The library can be accessed at https://github.com/ncu-dart/behavior2vec/.

Original languageEnglish
Article numbera43
JournalACM Transactions on Knowledge Discovery from Data
Volume12
Issue number4
DOIs
StatePublished - Jul 2018

Keywords

  • Behavior embedding
  • Collaborative filtering
  • Distributed representation
  • Learning representations
  • Matrix factorization
  • Word2Vec

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