Enhanced ID recommendation method utilizing semantic patterns of interactive behaviors
WANG Yuanlai1, BAI Yu1,2, LIAN Peng2,3
1. School of Computer Science, Shenyang Aerospace University, 110136, China; 2. National & Local Joint Engineering Laboratory of Multi-Language Collaborative Translation Technology, Shenyang 110136, China; 3. Shenyang Northern Software College of Information Technology, Shenyang 110136, China
Abstract：[Objective] ID-based recommendation methods in recommender systems utilize unique identifiers of users or items to generate suggestions. However, these methods often encounter challenges such as data sparsity and cold-start problems, especially when using single-domain data. Cross-domain ID-based recommendations can help mitigate cold-start issues by relying on overlapping users or items across different domains. However, cross-domain ID information often lacks overlapped users or items. To address this, latent semantic patterns in behavioral networks across various recommender domains can be leveraged. This method aims to extract user preferences for items from discrete ID data, thereby tackling the limited shared information between these domains. [Methods] Based on the study of interaction behaviors, this paper assumes the existence of latent pattern correlations between user-item interactions across different domains. A potential factor connects users across domains, leading some users to exhibit similar interaction behaviors in different contexts. These shared characteristics are referred to as interaction behavior semantic patterns. The proposed pattern-enhanced ID recommendation method enhances ID-based recommendations by leveraging these semantic patterns. In the target domain recommendation task, auxiliary domain information is introduced, and information from both auxiliary and target domains is jointly encoded using a graph neural network. By incorporating interaction behavior semantic patterns, user-item interaction and item description information from the auxiliary domain are transferred to the target domain. This process enhances the semantics of interaction behaviors in ID-based recommendations within the target domain. [Results] This study conducts experiments on nine public datasets. User-item ID interaction data from datasets such as Yelp2018, Amazon-Kindle, Alibaba-iFashion, Amazon-Electronic, Book Crossing, MovieLens10M, MovieLens20M, and MovieLens25M serve as target domain datasets. Meanwhile, item description data from the Citeulike-a dataset is used as the auxiliary domain dataset. There are no overlapping user or item IDs between these domains. Experimental results show that the proposed method outperforms the current state-of-the-art methods, showing improvements in Recall@20 by 3%-30% and in NDCG@20 by 1% to 40%. [Conclusions] This study proposes an ID recommendation method enhanced by interaction behavior semantic patterns based on the assumption of latent pattern correlations in user-item interactions across different domains. By introducing these semantic patterns, this method transfers user-item interaction information and item description information from the auxiliary domain to the target domain, thereby enhancing semantic understanding in ID-based recommendations within the target domain. Experimental results validate the ability of the proposed method to transfer semantic information in the absence of overlapping users and items across domains, yielding better recommendation performance. These findings validate the effectiveness of the proposed assumption and method. Additionally, experiments on ID recommendation tasks in multiple domains show that interaction behavior patterns between similar domains offer better transferability. The closer the auxiliary domain is to the target domain, the more notable the improvement in the target domain's ID recommendation results.
王远来, 白宇, 廉鹏. 基于交互行为语义模式增强的ID推荐方法[J]. 清华大学学报（自然科学版）, 2025, 65(5): 844-853.
WANG Yuanlai, BAI Yu, LIAN Peng. Enhanced ID recommendation method utilizing semantic patterns of interactive behaviors. Journal of Tsinghua University(Science and Technology), 2025, 65(5): 844-853.
[1] ZHAO Z H, FAN W Q, LI J T, et al. Recommender systems in the era of large language models (LLMs)[J]. IEEE Transactions on Knowledge and Data Engineering, 2024(1):1-20. [2] SHENG X R, ZHAO L Q, ZHOU G R, et al. One model to serve all:Star topology adaptive recommender for multi-domain CTR prediction[C]//Proceedings of the 30th ACM International Conference on Information&Knowledge Management. Queensland, Australia:Association for Computing Machinery, 2021:4104-4113. [3] YUAN G H, YUAN F J, LI Y D, et al. Tenrec:A large-scale multipurpose benchmark dataset for recommender systems[C]//Proceedings of the 36th International Conference on Neural Information Processing Systems. New Orleans, USA:Curran Associates Inc, 2022:834. [4] ZHAO Y, LI C Z, PENG J Q, et al. Beyond the overlapping users:Cross-domain recommendation via adaptive anchor link learning[C]//Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. Taipei, China:Association for Computing Machinery, 2023:1488-1497. [5] YUAN Z, YUAN F J, SONG Y, et al. Where to go next for recommender systems?ID-vs. modality-based recommender models revisited[C]//Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. Taipei, China:Association for Computing Machinery, 2023:2639-2649. [6] NAIM A. Consumer behavior in marketing patterns, types, segmentation[J]. European Journal of Economics, Finance and Business Development, 2023, 1(1):1-18. [7] ZHANG X K, XU B, REN Z C, et al. Disentangling ID and modality effects for session-based recommendation[C]//Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. Washington DC, USA:Association for Computing Machinery, 2024:1883-1892. [8] ZANG T Z, ZHU Y M, LIU H B, et al. A survey on cross-domain recommendation:Taxonomies, methods, and future directions[J]. ACM Transactions on Information Systems, 2022, 41(2):42. [9] KHAN M M, IBRAHIM R, GHANI I. Cross domain recommender systems:A systematic literature review[J]. ACM Computing Surveys (CSUR), 2017, 50(3):36. [10] MAN T, SHEN H W, JIN X L, et al. Cross-domain recommendation:An embedding and mapping approach[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17). Melbourne, Australia:AAAI Press, 2017:2464-2470. [11] LI P, TUZHILIN A. DDTCDR:Deep dual transfer cross domain recommendation[C]//Proceedings of the 13th International Conference on Web Search and Data Mining. Houston, USA:Association for Computing Machinery, 2020:331-339. [12] XIE R B, LIU Q, WANG L D, et al. Contrastive cross-domain recommendation in matching[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Washington DC, USA:Association for Computing Machinery, 2022:4226-4236. [13] WU J C, WANG X, FENG F L, et al. Self-supervised graph learning for recommendation[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA:Association for Computing Machinery, 2021:726-735. [14] HE X N, DENG K, WANG X, et al. LightGCN:Simplifying and powering graph convolution network for recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA:Association for Computing Machinery, 2020:639-648. [15] YU J L, XIA X, CHEN T, et al. XSimGCL:Towards extremely simple graph contrastive learning for recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(2):913-926. [16] ZHANG P, XIAO S, LIU Z, et al. Retrieve anything to augment large language models[J/OL]. Archive for Research Papers in the Fields of Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance, and Statistics.(2023-10-25)[2027-9-24] . DOI:https://doi.org/10.48550/arXiv.2310.07554. [17] HE R N, MCAULEY J. Ups and downs:Modeling the visual evolution of fashion trends with one-class collaborative filtering[C]//Proceedings of the 25th International Conference on World Wide Web. Montreal, Canada:International World Wide Web Conferences Steering Committee, 2016:507-517. [18] NI J M, LI J C, MCAULEY J. Justifying recommendations using distantly-labeled reviews and fine-grained aspects[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong, China:Association for Computational Linguistics, 2019:188-197. [19] HU Z, CAI S M, WANG J, et al. Collaborative recommendation model based on multi-modal multi-view attention network:Movie and literature cases[J]. Applied Soft Computing, 2023, 144:110518. [20] LEE J W, PARK S, LEE J, et al. Bilateral self-unbiased learning from biased implicit feedback[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. Madrid, Spain:Association for Computing Machinery, 2022:29-39. [21] YI J, REN X B, CHEN Z Z. Multi-auxiliary augmented collaborative variational auto-encoder for tag recommendation[J]. ACM Transactions on Information Systems, 2023, 41(4):106. [22] JI Y T, SUN A X, ZHANG J, et al. A critical study on data leakage in recommender system offline evaluation[J]. ACM Transactions on Information Systems, 2023, 41(3):75. [23] YU J L, YIN H Z, XIA X, et al. Are graph augmentations necessary?Simple graph contrastive learning for recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. Madrid, Spain:Association for Computing Machinery, 2022:1294-1303.
2025, Vol. 65 
