FineRec: Exploring Fine-grained Sequential Recommendation
Pages 1599 - 1608
Abstract
Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items, provide the potentials to capture user preferences and item characteristics at a fine-grained level. To this end, we propose a novel framework FineRec that explores the attribute-opinion pairs of reviews to finely handle sequential recommendation. Specifically, we utilize a large language model to extract attribute-opinion pairs from reviews. For each attribute, a unique attribute-specific user-opinion-item graph is created, where corresponding opinions serve as the edges linking heterogeneous user and item nodes. Afterwards, we devise a diversity-aware convolution operation to aggregate information within the graphs, enabling attribute-specific user and item representation learning. Ultimately, we present an interaction-driven fusion mechanism to integrate attribute-specific user/item representations across all attributes for generating recommendations. Extensive experiments conducted on several real-world datasets demonstrate the superiority of our FineRec over existing state-ofthe-art methods. Further analysis also verifies the effectiveness of our fine-grained manner in handling the task.
References
[1]
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. In RecSys. ACM, 1007--1014.
[2]
Renqin Cai, Jibang Wu, Aidan San, Chong Wang, and Hongning Wang. 2021. Category-aware Collaborative Sequential Recommendation. In SIGIR. 388--397.
[3]
Mohna Chakraborty, Adithya Kulkarni, and Qi Li. 2022. Open-Domain Aspect-Opinion Co-Mining with Double-Layer Span Extraction. In SIGKDD, Aidong Zhang and Huzefa Rangwala (Eds.). ACM, 66--75.
[4]
Qian Chen, Zhiqiang Guo, Jianjun Li, and Guohui Li. 2023. Knowledge-enhanced Multi-View Graph Neural Networks for Session-based Recommendation. In SIGIR. ACM, 352--361.
[5]
Yongjun Chen, Zhiwei Liu, Jia Li, Julian J. McAuley, and Caiming Xiong. 2022. Intent Contrastive Learning for Sequential Recommendation. In WWW. 2172--2182.
[6]
Zhiyong Cheng, Ying Ding, Xiangnan He, Lei Zhu, Xuemeng Song, and Mohan S. Kankanhalli. 2018. A^ 3NCF: An Adaptive Aspect Attention Model for Rating Prediction. In IJCAI. ijcai.org, 3748--3754.
[7]
Jin Yao Chin, Kaiqi Zhao, Shafiq R. Joty, and Gao Cong. 2018. ANR: Aspect-based Neural Recommender. In CIKM. ACM, 147--156.
[8]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT. 4171--4186.
[9]
Xin Dong, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Bo Zong, Dongjin Song, Yanchi Liu, Haifeng Chen, and Gerard de Melo. 2020. Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation. In AAAI. AAAI Press, 7667--7674.
[10]
Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Jianfeng Qu, Fuzhen Zhuang, Guanfeng Liu, Yanchi Liu, and Victor S. Sheng. 2023. Frequency Enhanced Hybrid Attention Network for Sequential Recommendation. In SIGIR. ACM, 78--88.
[11]
Shijie Geng, Juntao Tan, Shuchang Liu, Zuohui Fu, and Yongfeng Zhang. 2023. VIP5: Towards Multimodal Foundation Models for Recommendation. In Findings of EMNLP. Association for Computational Linguistics, 9606--9620.
[12]
Balá zs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. In ICLR.
[13]
Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, and Ji-Rong Wen. 2022. Towards Universal Sequence Representation Learning for Recommender Systems. In KDD. 585--593.
[14]
Hengchang Hu, Wei Guo, Yong Liu, and Min-Yen Kan. 2023. Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems. In CIKM. ACM, 843--853.
[15]
Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In SIGKDD, Won Kim, Ron Kohavi, Johannes Gehrke, and William DuMouchel (Eds.). ACM, 168--177.
[16]
Wang-Cheng Kang and Julian J. McAuley. 2018. Self-Attentive Sequential Recommendation. In ICDM. 197--206.
[17]
Kibum Kim, Dongmin Hyun, Sukwon Yun, and Chanyoung Park. 2023. MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation. In SIGIR. ACM, 68--77.
[18]
Chenliang Li, Xichuan Niu, Xiangyang Luo, Zhenzhong Chen, and Cong Quan. 2019. A Review-Driven Neural Model for Sequential Recommendation. In IJCAI. 2866--2872.
[19]
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural Attentive Session-based Recommendation. In CIKM. 1419--1428.
[20]
Jiacheng Li, Ming Wang, Jin Li, Jinmiao Fu, Xin Shen, Jingbo Shang, and Julian J. McAuley. 2023 b. Text Is All You Need: Learning Language Representations for Sequential Recommendation. In SIGKDD. ACM, 1258--1267.
[21]
Muyang Li, Zijian Zhang, Xiangyu Zhao, Wanyu Wang, Minghao Zhao, Runze Wu, and Ruocheng Guo. 2023 c. AutoMLP: Automated MLP for Sequential Recommendations. In WWW. ACM, 1190--1198.
[22]
Muyang Li, Xiangyu Zhao, Chuan Lyu, Minghao Zhao, Runze Wu, and Ruocheng Guo. 2022. MLP4Rec: A Pure MLP Architecture for Sequential Recommendations. In IJCAI. 2138--2144.
[23]
Xuewei Li, Aitong Sun, Mankun Zhao, Jian Yu, Kun Zhu, Di Jin, Mei Yu, and Ruiguo Yu. 2023 a. Multi-Intention Oriented Contrastive Learning for Sequential Recommendation. In WSDM. 411--419.
[24]
Yujie Lin, Chenyang Wang, Zhumin Chen, Zhaochun Ren, Xin Xin, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, and Pengjie Ren. 2023. A Self-Correcting Sequential Recommender. In WWW. ACM, 1283--1293.
[25]
Donghua Liu, Jing Li, Bo Du, Jun Chang, and Rong Gao. 2019. DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation. In KDD. ACM, 344--352.
[26]
Haibo Liu, Zhixiang Deng, Liang Wang, Jinjia Peng, and Shi Feng. 2023 a. Distribution-based Learnable Filters with Side Information for Sequential Recommendation. In RecSys. ACM, 78--88.
[27]
Sijia Liu, Jiahao Liu, Hansu Gu, Dongsheng Li, Tun Lu, Peng Zhang, and Ning Gu. 2023 b. AutoSeqRec: Autoencoder for Efficient Sequential Recommendation. In CIKM. ACM, 1493--1502.
[28]
Zhenghao Liu, Sen Mei, Chenyan Xiong, Xiaohua Li, Shi Yu, Zhiyuan Liu, Yu Gu, and Ge Yu. 2023 c. Text Matching Improves Sequential Recommendation by Reducing Popularity Biases. In CIKM. ACM, 1534--1544.
[29]
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. 2024. Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey. ACM Comput. Surv., Vol. 56, 2 (2024), 30:1--30:40.
[30]
Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao, Junhua Fang, Fuzhen Zhuang, Guanfeng Liu, Yanchi Liu, and Victor S. Sheng. 2023. Meta-optimized Contrastive Learning for Sequential Recommendation. In SIGIR. ACM, 89--98.
[31]
Yuyang Ren, Haonan Zhang, Qi Li, Luoyi Fu, Xinbing Wang, and Chenghu Zhou. 2023. Self-supervised Graph Disentangled Networks for Review-based Recommendation. In IJCAI. ijcai.org, 2288--2295.
[32]
Jie Shuai, Kun Zhang, Le Wu, Peijie Sun, Richang Hong, Meng Wang, and Yong Li. 2022. A Review-aware Graph Contrastive Learning Framework for Recommendation. In SIGIR. ACM, 1283--1293.
[33]
Jiayu Song, Jiajie Xu, Rui Zhou, Lu Chen, Jianxin Li, and Chengfei Liu. 2021. CBML: A Cluster-based Meta-learning Model for Session-based Recommendation. In CIKM. 1713--1722.
[34]
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In CIKM. 1441--1450.
[35]
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-Based Recommendation with Graph Neural Networks. In AAAI. 346--353.
[36]
Heeyoon Yang, YunSeok Choi, Gahyung Kim, and Jee-Hyong Lee. 2023. LOAM: Improving Long-tail Session-based Recommendation via Niche Walk Augmentation and Tail Session Mixup. In SIGIR. 527--536.
[37]
Yaowen Ye, Lianghao Xia, and Chao Huang. 2023. Graph Masked Autoencoder for Sequential Recommendation. In SIGIR. ACM, 321--330.
[38]
Peiyan Zhang, Jiayan Guo, Chaozhuo Li, Yueqi Xie, Jaeboum Kim, Yan Zhang, Xing Xie, Haohan Wang, and Sunghun Kim. 2023 a. Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network. In WSDM. 168--176.
[39]
Xiaokun Zhang, Hongfei Lin, Bo Xu, Chenliang Li, Yuan Lin, Haifeng Liu, and Fenglong Ma. 2022a. Dynamic intent-aware iterative denoising network for session-based recommendation. Inf. Process. Manag., Vol. 59, 3 (2022), 102936.
[40]
Xiaokun Zhang, Bo Xu, Chenliang Li, Yao Zhou, Liangyue Li, and Hongfei Lin. 2024 a. Side Information-Driven Session-based Recommendation: A Survey. arXiv preprint arXiv:2402.17129 (2024).
[41]
Xiaokun Zhang, Bo Xu, Fenglong Ma, Chenliang Li, Yuan Lin, and Hongfei Lin. 2023 c. Bi-preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation. ACM Trans. Inf. Syst., Vol. 42, 3, Article 68 (2023), bibinfonumpages28 pages.
[42]
Xiaokun Zhang, Bo Xu, Fenglong Ma, Chenliang Li, Liang Yang, and Hongfei Lin. 2024 b. Beyond Co-Occurrence: Multi-Modal Session-Based Recommendation. IEEE Trans. Knowl. Data Eng., Vol. 36, 4 (2024), 1450--1462.
[43]
Xiaokun Zhang, Bo Xu, Liang Yang, Chenliang Li, Fenglong Ma, Haifeng Liu, and Hongfei Lin. 2022b. Price DOES Matter!: Modeling Price and Interest Preferences in Session-based Recommendation. In SIGIR. 1684--1693.
[44]
Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In SIGIR. ACM, 83--92.
[45]
Yipeng Zhang, Xin Wang, Hong Chen, and Wenwu Zhu. 2023 b. Adaptive Disentangled Transformer for Sequential Recommendation. In KDD. ACM, 3434--3445.
[46]
Yichi Zhang, Guisheng Yin, and Yuxin Dong. 2023 d. Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation. In RecSys. ACM, 141--150.
[47]
Peilin Zhou, Qichen Ye, Yueqi Xie, Jingqi Gao, Shoujin Wang, Jae Boum Kim, Chenyu You, and Sunghun Kim. 2023. Attention Calibration for Transformer-based Sequential Recommendation. In CIKM. ACM, 3595--3605.
Index Terms
- FineRec: Exploring Fine-grained Sequential Recommendation
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Published In
July 2024
3164 pages
ISBN:9798400704314
DOI:10.1145/3626772
- General Chairs:
- Grace Hui Yang,
- Hongning Wang,
- Sam Han,
- Program Chairs:
- Claudia Hauff,
- Guido Zuccon,
- Yi Zhang
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Published: 11 July 2024
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SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 14 - 18, 2024
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