Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2018

Abstract

Our interactions with an application frequently leave a heterogeneous and contemporaneous trail of actions and adoptions (e.g., clicks, bookmarks, purchases). Given a sequence of a particular type (e.g., purchases)-- referred to as the target sequence, we seek to predict the next item expected to appear beyond this sequence. This task is known as next-item recommendation. We hypothesize two means for improvement. First, within each time step, a user may interact with multiple items (a basket), with potential latent associations among them. Second, predicting the next item in the target sequence may be helped by also learning from another supporting sequence (e.g., clicks). We develop three twin network structures modeling the generation of both target and support basket sequences. One based on "Siamese networks" facilitates full sharing of parameters between the two sequence types. The other two based on "fraternal networks" facilitate partial sharing of parameters. Experiments on real-world datasets show significant improvements upon baselines relying on one sequence type.

Keywords

Machine Learning, Learning, Preferences or Rankings, Recommender Systems

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | E-Commerce

Research Areas

Data Science and Engineering

Publication

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18): Stockholm, Sweden, July 13-19

First Page

3414

Last Page

3420

ISBN

9780999241127

Identifier

10.24963/ijcai.2018/474

Publisher

IJCAI

City or Country

Vienna

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.24963/ijcai.2018/474

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