Conference Proceeding Article
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.
Machine Learning, Learning Preferences or Rankings, Personalization and User Modeling
Databases and Information Systems | Data Storage Systems
Data Science and Engineering
Proceedings of the 26th International Joint Conference on Artificial Intelligence IJCAI-17, Melbourne, Australia, August 19-25
City or Country
LE, Duc Trong; LAUW, Hady W.; and FANG, Yuan.
Basket-sensitive personalized item recommendation. (2017). Proceedings of the 26th International Joint Conference on Artificial Intelligence IJCAI-17, Melbourne, Australia, August 19-25. 2060-2066. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3765
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.