2000 character limit reached
Within-basket Recommendation via Neural Pattern Associator (2401.16433v2)
Published 25 Jan 2024 in cs.IR and cs.LG
Abstract: Within-basket recommendation (WBR) refers to the task of recommending items to the end of completing a non-empty shopping basket during a shopping session. While the latest innovations in this space demonstrate remarkable performance improvement on benchmark datasets, they often overlook the complexity of user behaviors in practice, such as 1) co-existence of multiple shopping intentions, 2) multi-granularity of such intentions, and 3) interleaving behavior (switching intentions) in a shopping session. This paper presents Neural Pattern Associator (NPA), a deep item-association-mining model that explicitly models the aforementioned factors. Specifically, inspired by vector quantization, the NPA model learns to encode common user intentions (or item-combination patterns) as quantized representations (a.k.a. codebook), which permits identification of users's shopping intentions via attention-driven lookup during the reasoning phase. This yields coherent and self-interpretable recommendations. We evaluated the proposed NPA model across multiple extensive datasets, encompassing the domains of grocery e-commerce (shopping basket completion) and music (playlist extension), where our quantitative evaluations show that the NPA model significantly outperforms a wide range of existing WBR solutions, reflecting the benefit of explicitly modeling complex user intentions.
- Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB. Santiago, Chile.
- A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery Shopping. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining.
- Oren Barkan and Noam Koenigstein. 2016. Item2vec: neural item embedding for collaborative filtering. In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE.
- Controllable multi-interest framework for recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
- Multi-interest diversification for end-to-end sequential recommendation. ACM Transactions on Information Systems (TOIS) (2021).
- Intent contrastive learning for sequential recommendation. In Proceedings of the ACM Web Conference 2022.
- Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019.
- Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.
- Karam Gouda and Mohammed Javeed Zaki. 2001. Efficiently mining maximal frequent itemsets. In Proceedings 2001 IEEE International Conference on Data Mining. IEEE.
- E-commerce in your inbox: Product recommendations at scale. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining.
- Learning Multi-granularity Consecutive User Intent Unit for Session-based Recommendation. In Proceedings of the fifteenth ACM International conference on web search and data mining.
- Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems.
- Multiple interleaving interests modeling of sequential user behaviors in e-commerce platform. World Wide Web 24, 4 (2021), 1121–1146.
- Analyzing and predicting purchase intent in e-commerce: anonymous vs. identified customers. In eCOM 2020: The 2020 SIGIIR Workshop on eCommerce. ACM.
- Autoregressive diffusion models. In 10th International Conference on Learning Representations. ICLR.
- Categorical reparameterization with gumbel-softmax. In 5th International Conference on Learning Representations, ICLR 2017, Conference Track Proceedings.
- Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM). IEEE.
- Basket-sensitive personalized item recommendation. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. IJCAI.
- GPT4Rec: A generative framework for personalized recommendation and user interests interpretation. arXiv preprint arXiv:2304.03879 (2023).
- Modeling personalized representation for within-basket recommendation based on deep learning. Expert Systems with Applications (2022).
- Variational Autoencoders for Collaborative Filtering. In Proceedings of the 2018 World Wide Web Conference on World Wide Web, Lyon, France. ACM.
- Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing (2003).
- Basket recommendation with multi-intent translation graph neural network. In 2020 IEEE International Conference on Big Data (Big Data).
- Basconv: Aggregating heterogeneous interactions for basket recommendation with graph convolutional neural network. In Proceedings of the 2020 SIAM International Conference on Data Mining. SIAM.
- Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017).
- The concrete distribution: A continuous relaxation of discrete random variables. In 5th International Conference on Learning Representations, ICLR 2017, Conference Track Proceedings.
- Efficient estimation of word representations in vector space. In 1st International Conference on Learning Representations, ICLR 2013, Workshop Track Proceedings.
- Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems (2013).
- Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP).
- Improving language understanding by generative pre-training. (2018).
- Abhishek Saxena and Navneet K Gaur. 2015. Frequent item set based recommendation using apriori. International Journal of Science, Engineering and Technology Research (2015).
- Temporal aware multi-interest graph neural network for session-based recommendation. In Asian Conference on Machine Learning. PMLR.
- Capturing multi-granularity interests with capsule attentive network for sequential recommendation. In Web Information Systems Engineering–WISE 2021: 22nd International Conference on Web Information Systems Engineering, WISE 2021. Springer.
- Ryan Strauss and Junier B Oliva. 2021. Arbitrary conditional distributions with energy. Advances in Neural Information Processing Systems (2021).
- BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management.
- Selecting the right objective measure for association analysis. Information Systems (2004).
- A deep and tractable density estimator. In International Conference on Machine Learning. PMLR.
- Neural discrete representation learning. Advances in neural information processing systems (2017).
- Meta-prod2vec: Product embeddings using side-information for recommendation. In Proceedings of the 10th ACM conference on recommender systems.
- Attention is all you need. Advances in neural information processing systems (2017).
- Representing and recommending shopping baskets with complementarity, compatibility and loyalty. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management.
- Watermarking for Out-of-distribution Detection. arXiv preprint arXiv:2210.15198 (2022).
- Learning intents behind interactions with knowledge graph for recommendation. In Proceedings of the Web Conference 2021.
- Knowledge-aware complementary product representation learning. In Proceedings of the 13th International Conference on Web Search and Data Mining.
- Complementary recommendations: A brief survey. In 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS). IEEE.