{"type":"ScholarlyArticle","title":"Basket Representation Learning by Hypergraph Convolution on Repeated Items for Next-basket Recommendation","authors":[{"name":"Yalin Yu","slug":"yalin-yu","given":"Yalin","family":"Yu","affiliation":"Software College , Northeastern University , China"},{"name":"Enneng Yang","slug":"enneng-yang","given":"Enneng","family":"Yang","affiliation":"Software College , Northeastern University , China"},{"name":"Guibing Guo","slug":"guibing-guo","given":"Guibing","family":"Guo","affiliation":"Software College , Northeastern University , China"},{"name":"Linying Jiang","slug":"linying-jiang","given":"Linying","family":"Jiang","affiliation":"Software College , Northeastern University , China"},{"name":"Xingwei Wang","slug":"xingwei-wang","given":"Xingwei","family":"Wang","affiliation":"School of Computer Science and Engineering , Northeastern University , China"}],"abstract":"Basket representation plays an important role in the task of next-basket recommendation. However, existing methods generally adopts pooling operations to learn a basket's representation, from which two critical issues can be identified. \r\nFirst, they treat a basket as a set of items independent and identically distributed. We find that items occurring in the same basket have much higher correlations than those randomly selected by conducting data analysis on a real dataset. \r\nSecond, although some works have recognized the importance of items repeatedly purchased in multiple baskets, they ignore the correlations among the repeated items in a same basket, whose importance is  shown by our data analysis. In this paper, we propose a novel Basket Representation Learning (BRL) model by leveraging the correlations among intra-basket items. Specifically, we first connect all the items (in a basket) as a hyperedge, where the correlations among different items can be well exploited by hypergraph convolution operations. Meanwhile, we also connect all the repeated items in the same basket as a hyperedge, whereby their correlations can be further strengthened. We generate a negative (positive) view of the basket by data augmentation on repeated (non-repeated) items, and apply contrastive learning to force more agreements on repeated items. Finally, experimental results on three real datasets show that our approach performs better than eight baselines in ranking accuracy.","doi":"10.24963/ijcai.2023/268","url":"https://ijcai.org/proceedings/2023/268","pdf":"https://ijcai.org/proceedings/2023/0268.pdf","markdown":"https://ijcai.org/proceedings/2023/268.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/268","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Data Mining","track":"Main Track","number":268,"pages":{"first":2415,"last":2422},"keywords":["Data Mining: DM: Recommender systems","Data Mining: DM: Information retrieval"],"videos":[],"references":[{"position":1,"raw":"Mozhdeh Ariannezhad, Sami Jullien, Ming Li, Min Fang, Se- bastian Schelter, and Maarten de Rijke. ReCANet: A Re- peat Consumption-Aware Neural Network for Next Bas- ket Recommendation in Grocery Shopping. In SIGIR '22, pages 1240-1250, Madrid Spain, July 2022. ACM.","title":"ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping","authors":"Mozhdeh Ariannezhad, Sami Jullien, Ming Li, Min Fang, Sebastian Schelter, Maarten De Rijke","year":"2022","venue":"SIGIR '22","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"Ting Bai, Jian-Yun Nie, Wayne Xin Zhao, Yutao Zhu, Pan Du, and Ji-Rong Wen. An Attribute-aware Neural Atten- tive Model for Next Basket Recommendation. In SIGIR '18, pages 1201-1204, Ann Arbor MI USA, June 2018. ACM.","title":"An Attribute-aware Neural Attentive Model for Next Basket Recommendation","authors":"Ting Bai, Jian-Yun Nie, Wayne Xin Zhao, Yutao Zhu, Pan Du, Ji-Rong Wen","year":"2018","venue":"SIGIR '18","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"Song Bai, Feihu Zhang, and Philip H.S. Torr. Hypergraph convolution and hypergraph attention. Pattern Recogni- tion, 110:107637, February 2021.","title":"Hypergraph convolution and hypergraph attention","authors":"Song Bai, Feihu Zhang, Philip H S Torr","year":"2021","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling, December 2014. arXiv:1412.3555 [cs].","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","authors":"Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, Yoshua Bengio","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"Guglielmo Faggioli, Mirko Polato, and Fabio Aiolli. Recency Aware Collaborative Filtering for Next Basket Recommen- dation. In UMAP '20, pages 80-87, Genoa Italy, July 2020. ACM.","title":"Recency Aware Collaborative Filtering for Next Basket Recommendation","authors":"Guglielmo Faggioli, Mirko Polato, Fabio Aiolli","year":"2020","venue":"UMAP '20","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"Guglielmo Faggioli, Mirko Polato, and Fabio Aiolli. Recency Aware Collaborative Filtering for Next Basket Recommen- dation. In UMAP '20, pages 80-87, Genoa Italy, July 2020. ACM.","title":"Recency Aware Collaborative Filtering for Next Basket Recommendation","authors":"Guglielmo Faggioli, Mirko Polato, Fabio Aiolli","year":"2020","venue":"UMAP '20","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. Hypergraph Neural Networks, February 2019. arXiv:1809.09401 [cs, stat].","title":"Hypergraph Neural Networks","authors":"Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"Lukáš Havrlant and Vladik Kreinovich. A simple probabilis- tic explanation of term frequency-inverse document fre- quency (tf-idf) heuristic (and variations motivated by this explanation). International Journal of General Systems, 46(1):27-36, January 2017.","title":"A simple probabilistic explanation of term frequency-inverse document frequency (tf-idf) heuristic (and variations motivated by this explanation)","authors":"Lukáš Havrlant, Vladik Kreinovich","year":"2017","venue":"International Journal of General Systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"Sepp Hochreiter and Jürgen Schmidhuber. Long Short-Term Memory. Neural Computation, 9(8):1735-1780, Novem- ber 1997.","title":"Long Short-Term Memory","authors":"Sepp Hochreiter, Jürgen Schmidhuber","year":"1997","venue":"Neural Computation","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"Haoji Hu and Xiangnan He. Sets2Sets: Learning from Se- quential Sets with Neural Networks. In KDD '19, pages 1491-1499, Anchorage AK USA, July 2019. ACM.","title":"Sets2Sets: Learning from Sequential Sets with Neural Networks","authors":"Haoji Hu, Xiangnan He","year":"2019","venue":"KDD '19","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"Haoji Hu, Xiangnan He, Jinyang Gao, and Zhi-Li Zhang. Modeling Personalized Item Frequency Information for Next-basket Recommendation. In SIGIR '20, pages 1071- 1080, Virtual Event China, July 2020. ACM.","title":"Modeling Personalized Item Frequency Information for Next-basket Recommendation","authors":"Haoji Hu, Xiangnan He, Jinyang Gao, Zhi-Li Zhang","year":"2020","venue":"Virtual Event China","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"Ori Katz, Oren Barkan, Noam Koenigstein, and Nir Zabari. Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation. In RecSys '22, pages 316-326, Seattle WA USA, September 2022. ACM.","title":"Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation","authors":"Ori Katz, Oren Barkan, Noam Koenigstein, Nir Zabari","year":"2022","venue":"RecSys '22","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"Duc-Trong Le, Hady W. Lauw, and Yuan Fang. Correlation- Sensitive Next-Basket Recommendation. In IJCAI-19, pages 2808-2814, Macao, China, August 2019.","title":"Correlation-Sensitive Next-Basket Recommendation","authors":"Duc-Trong Le, Hady W Lauw, Yuan Fang","year":"2019","venue":"IJCAI-19","doi":null,"url":null,"internal_url":"/proceedings/2019/389"},{"position":14,"raw":"Yuqi Qin, Pengfei Wang, and Chenliang Li. The World is Binary: Contrastive Learning for Denoising Next Basket Recommendation. In SIGIR '21, pages 859-868, Virtual Event Canada, July 2021. ACM.","title":"The World is Binary: Contrastive Learning for Denoising Next Basket Recommendation","authors":"Yuqi Qin, Pengfei Wang, Chenliang Li","year":"2021","venue":"SIGIR '21","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt- Thieme. Factorizing personalized Markov chains for next- basket recommendation. In WWW '10, page 811, Raleigh, North Carolina, USA, 2010. ACM Press.","title":"Factorizing personalized Markov chains for nextbasket recommendation","authors":"Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme","year":"2010","venue":"WWW '10","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"Alexander Schulz, Fabian Hinder, and Barbara Hammer. DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction. In IJCAI-PRICAI-20, pages 2305-2311, Yokohama, Japan, July 2020.","title":"DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction","authors":"Alexander Schulz, Fabian Hinder, Barbara Hammer","year":"2020","venue":"IJCAI-PRICAI-20","doi":null,"url":null,"internal_url":"/proceedings/2020/319"},{"position":17,"raw":"Zhufeng Shao, Shoujin Wang, Qian Zhang, Wenpeng Lu, Zhao Li, and Xueping Peng. A Systematical Evaluation for Next-Basket Recommendation Algorithms, September 2022. arXiv:2209.02892 [cs.","title":"A Systematical Evaluation for Next-Basket Recommendation Algorithms","authors":"Zhufeng Shao, Shoujin Wang, Qian Zhang, Wenpeng Lu, Zhao Li, Xueping Peng","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"Yanyan Shen, Baoyuan Ou, and Ranzhen Li. MBN: Towards Multi-Behavior Sequence Modeling for Next Basket Rec- ommendation. TKDD'22, 16(5):1-23, October 2022.","title":"MBN: Towards Multi-Behavior Sequence Modeling for Next Basket Recommendation","authors":"Yanyan Shen, Baoyuan Ou, Ranzhen Li","year":"2022","venue":"TKDD'","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, and Xiangliang Zhang. Self-Supervised Hypergraph Convolutional Networks for Session-based Recommenda- tion. AAAI, 35(5):4503-4511, May 2021.","title":"Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation","authors":"Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, Xiangliang Zhang","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"Jaehyuk Yi and Jinkyoo Park. Hypergraph Convolutional Re- current Neural Network. In KDD '20, pages 3366-3376, Virtual Event CA USA, August 2020. ACM.","title":"Hypergraph Convolutional Recurrent Neural Network","authors":"Jaehyuk Yi, Jinkyoo Park","year":"2020","venue":"KDD '20","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"Feng Yu, Qiang Liu, and Shu Wu. A Dynamic Recurrent Model for Next Basket Recommendation. In SIGIR '16, pages 729-732, Pisa Italy, July 2016. ACM.","title":"A Dynamic Recurrent Model for Next Basket Recommendation","authors":"Feng Yu, Qiang Liu, Shu Wu","year":"2016","venue":"SIGIR '16","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"Junliang Yu, Hongzhi Yin, and Jundong Li. Self-Supervised Multi-Channel Hypergraph Convolutional Network for So- cial Recommendation. In WWW '21, pages 413-424, Ljubljana Slovenia, April 2021. ACM.","title":"Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation","authors":"Junliang Yu, Hongzhi Yin, Jundong Li","year":"2021","venue":"WWW '21","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"Shengyu Zhang, Dong Yao, Zhou Zhao, Tat-Seng Chua, and Fei Wu. CauseRec: Counterfactual User Sequence Synthe- sis for Sequential Recommendation. In SIGIR '21, pages 367-377, Virtual Event Canada, July 2021. ACM.","title":"CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation","authors":"Shengyu Zhang, Dong Yao, Zhou Zhao, Tat-Seng Chua, Fei Wu","year":"2021","venue":"SIGIR '21","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}