Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2017

Abstract

Personalized item recommendation is useful in narrowing down the list of options provided to a user. In this paper, we address the problem scenario where the user is currently holding a basket of items, and the task is to recommend an item to be added to the basket. Here, we assume that items currently in a basket share some association based on an underlying latent need, e.g., ingredients to prepare some dish, spare parts of some device. Thus, it is important that a recommended item is relevant not only to the user, but also to the existing items in the basket. Towards this goal, we propose two approaches. First, we explore a factorization-based model called BFM that incorporates various types of associations involving the user, the target item to be recommended, and the items currently in the basket. Second, based on our observation that various recommendations towards constructing the same basket should have similar likelihoods, we propose another model called CBFM that further incorporates basket-level constraints. Experiments on three real-life datasets from different domains empirically validate these models against baselines based on matrix factorization and association rules.

Keywords

Machine Learning, Learning Preferences or Rankings, Personalization and User Modeling

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

IJCAI-17: Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, August 19-25

First Page

2060

Last Page

2066

ISBN

9780999241103

Identifier

10.24963/ijcai.2017/286

Publisher

IJCAI

City or Country

Vienna

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.24963/ijcai.2017/286

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