Quality-aware neural complementary item recommendation Conference Paper uri icon


  • © 2018 Association for Computing Machinery. Complementary item recommendation finds products that go well with one another (e.g., a camera and a specific lens). While complementary items are ubiquitous, the dimensions by which items go together can vary by both product and category, making it difficult to detect complementary items at scale. Moreover, in practice, user preferences for complementary items can be complex combinations of item quality and evidence of complementarity. Hence, we propose a new neural complementary recommender Encore that can jointly learn complementary item relationships and user preferences. Specifically, Encore (i) effectively combines and balances both stylistic and functional evidence of complementary items across item categories; (ii) naturally models item latent quality for complementary items through Bayesian inference of customer ratings; and (iii) builds a novel neural network model to learn the complex (non-linear) relationships between items for flexible and scalable complementary product recommendations. Through experiments over large Amazon datasets, we find that Encore effectively learns complementary item relationships, leading to an improvement in accuracy of 15.5% on average versus the next-best alternative.

author list (cited authors)

  • Zhang, Y., Lu, H., Niu, W., & Caverlee, J.

citation count

  • 12

editor list (cited editors)

  • Pera, S., Ekstrand, M. D., Amatriain, X., & O'Donovan, J.

publication date

  • September 2018


  • ACM  Publisher