Modeling Personalized Item Frequency Information for Next-basket Recommendation

• URL: http://arxiv.org/abs/2006.00556v1
• Date: Sun, 31 May 2020 16:42:39 GMT
• Title: Modeling Personalized Item Frequency Information for Next-basket Recommendation
• Authors: Haoji Hu and Xiangnan He and Jinyang Gao and Zhi-Li Zhang
• Abstract summary: Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. We argue that existing RNNs cannot directly capture item frequency information in the recommendation scenario. We propose a simple item frequency based k-nearest neighbors (kNN) method to directly utilize these critical signals.
• Score: 63.94555438898309
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. In this scenario, a user purchases a set of items (a basket) at a time. NBR performs sequential modeling and recommendation based on a sequence of baskets. NBR is in general more complex than the widely studied sequential (session-based) recommendation which recommends the next item based on a sequence of items. Recurrent neural network (RNN) has proved to be very effective for sequential modeling and thus been adapted for NBR. However, we argue that existing RNNs cannot directly capture item frequency information in the recommendation scenario. Through careful analysis of real-world datasets, we find that {\em personalized item frequency} (PIF) information (which records the number of times that each item is purchased by a user) provides two critical signals for NBR. But, this has been largely ignored by existing methods. Even though existing methods such as RNN based methods have strong representation ability, our empirical results show that they fail to learn and capture PIF. As a result, existing methods cannot fully exploit the critical signals contained in PIF. Given this inherent limitation of RNNs, we propose a simple item frequency based k-nearest neighbors (kNN) method to directly utilize these critical signals. We evaluate our method on four public real-world datasets. Despite its relative simplicity, our method frequently outperforms the state-of-the-art NBR methods -- including deep learning based methods using RNNs -- when patterns associated with PIF play an important role in the data.

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