Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2019

Abstract

Items adopted by a user over time are indicative ofthe underlying preferences. We are concerned withlearning such preferences from observed sequencesof adoptions for recommendation. As multipleitems are commonly adopted concurrently, e.g., abasket of grocery items or a sitting of media consumption, we deal with a sequence of baskets asinput, and seek to recommend the next basket. Intuitively, a basket tends to contain groups of relateditems that support particular needs. Instead of recommending items independently for the next basket, we hypothesize that incorporating informationon pairwise correlations among items would help toarrive at more coherent basket recommendations.Towards this objective, we develop a hierarchicalnetwork architecture codenamed Beacon to modelbasket sequences. Each basket is encoded takinginto account the relative importance of items andcorrelations among item pairs. This encoding isutilized to infer sequential associations along thebasket sequence. Extensive experiments on threepublic real-life datasets showcase the effectivenessof our approach for the next-basket recommendation problem.

Keywords

Learning Preferences, Rankings, Recommender Systems

Discipline

Databases and Information Systems | Operations Research, Systems Engineering and Industrial Engineering | Sales and Merchandising

Research Areas

Data Science and Engineering

Publication

Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macau, China, 2019 August 10-16

First Page

2808

Last Page

2814

ISBN

9780999241141

Identifier

10.24963/ijcai.2019/389

Publisher

International Joint Conferences on Artificial Intelligence

City or Country

Los Altos, CA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.24963/ijcai.2019/389

Download

Share

COinS