Behavior2Vec

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/.

• Publication year

  2018

• Venue

  ACM Transactions on Knowledge Discovery from Data

• Publication date

  2018-04-16

• Fields of study

  Business, Computer Science

• Identifiers
  DOI 10.1145/3184454
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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