M2: Mixed Models With Preferences, Popularities and Transitions for Next-Basket Recommendation

被引:0
作者
Peng, Bo [1 ]
Ren, Zhiyun [2 ]
Parthasarathy, Srinivasan [3 ,4 ]
Ning, Xia [3 ,4 ]
机构
[1] Ohio State Univ, Dept Comp Sci & Engn, Columbus, OH 43210 USA
[2] Ohio State Univ, Dept Biomed Informat, Columbus, OH 43210 USA
[3] Ohio State Univ, Dept Biomed Informat, Dept Comp Sci & Engn, Columbus, OH 43210 USA
[4] Ohio State Univ, Translat Data Analyt Inst, Columbus, OH 43210 USA
基金
美国国家科学基金会;
关键词
Recurrent neural networks; Task analysis; Benchmark testing; Adaptation models; Decoding; Protocols; Markov processes; Recommender systems; next-basket recommendation; encoder-decoder architecture; mixed models;
D O I
10.1109/TKDE.2022.3142773
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M-2 ) for the next-basket recommendation. This method models three important factors in next-basket generation process: 1) users' general preferences, 2) items' global popularities and 3) transition patterns among items. Unlike existing recurrent neural network-based approaches, M-2 does not use the complicated networks to model the transitions among items, or generate embeddings for users. Instead, it has a simple encoder-decoder based approach (ed-Trans ) to better model the transition patterns among items. We compared M-2 with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket. Our experimental results demonstrate that M-2 significantly outperforms the state-of-the-art methods on all the datasets in all the tasks, with an improvement of up to 22.1%. In addition, our ablation study demonstrates that the ed-Trans is more effective than recurrent neural networks in terms of the recommendation performance. We also have a thorough discussion on various experimental protocols and evaluation metrics for next-basket recommendation evaluation.
引用
收藏
页码:4033 / 4046
页数:14
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