Survey on Bundle Recommendation Algorithms

doi:10.3969/j.issn.1006-2475.2025.09.001

Abstract

Abstract:
Abstract： Bundle recommendation refers to optimizing and recommending the best solution by combining multiple related goods， services， or content， which can meet the various needs of users. With the rapid development of sectors like e-commerce and travel retail， bundle recommendation has become an important approach to improve user experience and business benefits. This paper reviews the research progress and application status of bundle recommendation algorithms. Firstly， the task definition， task characteristics， task challenges， and commonly used evaluation metrics are clarified. The task challenges include the integrity of bundled packages， diversity of bundled packages， data sparsity， cold start problems， and bundle generation problems. Secondly， the existing algorithms are classified into three major categories， data mining-based algorithms， traditional machine learning-based algorithms， deep learning-based algorithms， and further sorted out into seven subcategories. The characteristics of each category are thoroughly analyzed. Thirdly， commonly used datasets for the bundle recommendation task are summarized. Finally， the future development trends of bundle recommendation are discussed.

Key words: Key words： bundle recommendation, combinatorial optimization, data mining, deep learning, machine learning

CLC Number:

TP391

LI Xiongqing1, 2, PENG Mingtian1, 2, LI Yong1, 2, WANG Junfei1, 2, LIU Dezhi1, 3, BIAN Yuxuan1, 3, CHAI Yuelin1, 3, LIU Yuntao1, 3. Survey on Bundle Recommendation Algorithms[J]. Computer and Modernization, 2025, 0(09): 1-13.

References

［1］ LIU G R， ZHANG X Z. Collaborative filtering based recommendation system for product bundling［C］// 2006 International Conference on Management Science and Engineering. IEEE， 2006：251-254.
［2］ ZHU T， HARRINGTON P， LI J J， et al. Bundle recommendation in ecommerce［C］// Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM， 2014：657-666.
［3］ GARFINKEL R， GOPAL R， TRIPATHI A， et al. Design of a shopbot and recommender system for bundle purchases［J］. Decision Support Systems， 2006，42（3）：1974-1986.
［4］ XIE M， LAKSHMANAN L V S， WOOD P T. Breaking out of the box of recommendations： From items to packages［C］// Proceedings of the 4th ACM Conference on Recommender Systems. ACM， 2010：151-158.
［5］ ZANKER M， ASCHINGER M， JESSENITSCHNIG M. Development of a collaborative and constraint-based Web configuration system for personalized bundling of products and services［C］// International Conference on Web Information Systems Engineering. Springer， 2007：273-284.
［6］ ZANKER M， ASCHINGER M， JESSENITSCHNIG M. Constraint-based personalised configuring of product and service bundles［J］. International Journal of Mass Customisation， 2010，3（4）：407-425.
［7］ DENG T， FAN W F， GEERTS F. On the complexity of package recommendation problems［C］// Proceedings of the 31st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. ACM， 2012：261-272.
［8］ PARAMESWARAN A， VENETIS P， GARCIA-MOLINA H. Recommendation systems with complex constraints： A course recommendation perspective［J］. ACM Transactions on Information Systems （TOIS）， 2011，29（4）：1-33.
［9］ LIU G N， FU Y J， CHEN G Q， et al. Modeling buying motives for personalized product bundle recommendation［J］. ACM Transactions on Knowledge Discovery from Data （TKDD）， 2017，11（3）：1-26.
［10］ BELADEV M， ROKACH L， SHAPIRA B. Recommender systems for product bundling［J］. Knowledge-Based Systems， 2016，111：193-206.
［11］ WANG Z H， JIANG Z H， REN Z C， et al. A path-constrained framework for discriminating substitutable and complementary products in e-commerce［C］// Proceedings of the 11th ACM International Conference on Web Search and Data Mining. ACM， 2018：619-627.
［12］ BEHESHTIAN-ARDAKANI A， FATHIAN M， GHOLAMI
AN M. A novel model for product bundling and direct marketing in e-commerce based on market segmentation［J］. Decision Science Letters， 2018，7（1）：39-54.
［13］ FANG Y， XIAO X Y， WANG X Y， et al. Customized bundle recommendation by association rules of product categories for online supermarkets［C］// 2018 IEEE 3rd International Conference on Data Science in Cyberspace （DSC）. IEEE， 2018：472-475.
［14］ LIU Q， CHEN E H， XIONG H， et al. A cocktail approach for travel package recommendation［J］. IEEE Transactions on Knowledge and Data Engineering， 2012，26（2）：278-293.
［15］ MCAULEY J， PANDEY R， LESKOVEC J. Inferring networks of substitutable and complementary products［C］// Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM， 2015：785-794.
［16］ LIU Y D， XIE M， LAKSHMANAN L V S. Recommending user generated item lists［C］// Proceedings of the 8th ACM Conference on Recommender systems. ACM， 2014：185-192.
［17］ GE Y， XIONG H， TUZHILIN A， et al. Cost-aware collaborative filtering for travel tour recommendations［J］. ACM Transactions on Information Systems （TOIS）， 2014，32（1）：1-31.
［18］ PATHAK A， GUPTA K， MCAULEY J. Generating and personalizing bundle recommendations on steam［C］// Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM， 2017：1073-1076.
［19］ CAO D， NIE L Q， HE X N， et al. Embedding factorization models for jointly recommending items and user generated lists［C］// Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM， 2017：585-594.
［20］ PAN Y H， ZHANG Y F， ZHANG R. Combo-recommendation based on potential relevance of items［C］// The 18th Asia-Pacific Web Conference on Web Technologies and Applications. Springer， 2016：505-517.
［21］ AVNY BROSH T， LIVNE A， SAR SHALOM O， et al. BRUCE： Bundle recommendation using contextualized item embeddings［C］// Proceedings of the 16th ACM Conference on Recommender Systems. ACM， 2022：237-245.
［22］ GONG Y， ZHU Y， DUAN L， et al. Exact-k recommendation via maximal clique optimization［C］// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM， 2019：617-626.
［23］ HAO J H， ZHAO T， LI J， et al. P-companion： A principled framework for diversified complementary product recommendation［C］// Proceedings of the 29th ACM International Conference on Information & Knowledge Management. ACM， 2020：2517-2524.
［24］ LI Z， WU B， LIU Q， et al. Learning the compositional visual coherence for complementary recommendations［J］. arXiv preprint arXiv：2006.04380， 2020.
［25］ HE Y， WANG J L， NIU W， et al. A hierarchical self-attentive model for recommending user-generated item lists［C］// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. ACM， 2019：1481-1490.
［26］ HE Y， ZHANG Y， LIU W W， et al. Consistency-aware recommendation for user-generated item list continuation［C］// Proceedings of the 13th International Conference on Web Search and Data Mining. ACM， 2020：250-258.
［27］ CHEN L， LIU Y， HE X N， et al. Matching user with item set： Collaborative bundle recommendation with deep attention network［C］// Proceedings of the 28th International Joint Conference on Artificial Intelligence. ACM， 2019：2095-2101.
［28］ WEI P H， LIU S G， YANG X H， et al. Towards personalized bundle creative generation with contrastive non-autoregressive decoding［C］// Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM， 2022：2634-2638.
［29］ BAI J Z， ZHOU C， SONG J S， et al. Personalized bundle list recommendation［C］// The World Wide Web Conference. ACM， 2019：60-71.
［30］ KOUKI P， FOUNTALIS I， VASILOGLOU N， et al. Product collection recommendation in online retail［C］// Proceedings of the 13th ACM Conference on Recommender Systems. ACM， 2019：486-490.
［31］ HAN X T， WU Z X， JIANG Y G， et al. Learning fashion compatibility with bidirectional lstms［C］// Proceedings of the 25th ACM International Conference on Multimedia. ACM， 2017：1078-1086.
［32］ LIU Q J， ZHU J M， WU J H， et al. FANS： Fast non-autoregressive sequence generation for item list continuation［C］// Proceedings of the ACM Web Conference. ACM， 2023：3309-3318.
［33］ LIU S C， CAI Q P， HE Z K， et al. Generative flow network for listwise recommendation［C］// Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM， 2023：1524-1534.
［34］ ZHAO S， WEI W， ZOU D， et al. Multi-view intent disentangle graph networks for bundle recommendation［J］. AAAI Conference on Artificial Intelligence， 2022，36（4）：4379-4387.
［35］ DENG Q L， WANG K， ZHAO M H， et al. Personalized bundle recommendation in online games［C］// Proceedings of the 29th ACM International Conference on Information & Knowledge Management. ACM， 2020：2381-2388.
［36］ CHANG J X， GAO C， HE X N， et al. Bundle recommendation with graph convolutional networks［C］// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM， 2020：1673-1676.
［37］ CUI Z Y， LI Z K， WU S， et al. Dressing as a whole： Outfit compatibility learning based on node-wise graph neural networks［C］// The World Wide Web Conference. ACM， 2019：307-317.
［38］ LI X C， WANG X， HE X N， et al. Hierarchical fashion graph network for personalized outfit recommendation［C］// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM， 2020：159-168.
［39］ MA Y S， HE Y Z， ZHANG A， et al. CrosscBR： Cross-view contrastive learning for bundle recommendation［C］// Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM， 2022：1233-1241.
［40］ MA Y S， HE Y Z， WANG X， et al. MultiCBR： Multi-view contrastive learning for bundle recommendation［J］. ACM Transactions on Information Systems， 2024，42（4）：1-23.
［41］ REN Y Y， ZHANG H N， FU L Y， et al. Distillation-enhanced graph masked autoencoders for bundle recommendation［C］// Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM， 2023：1660-1669.
［42］ ZHANG Z N， DU B X， TONG H H. Suger： A subgraph-based graph convolutional network method for bundle recommendation［C］// Proceedings of the 31st ACM International Conference on Information & Knowledge Management. ACM， 2022：4712-4716.
［43］ WANG X， LIU X， LIU J， et al. Relational graph neural network with neighbor interactions for bundle recommendation service［C］// 2021 IEEE International Conference on Web Services （ICWS）. IEEE， 2021：167-172.
［44］ DU X Y， QIAN K， MA Y S， et al. Enhancing item-level bundle representation for bundle recommendation［J］. arXiv preprint arXiv：2311.16892， 2023.
［45］ WEI Y W， LIU X H， MA Y S， et al. Strategy-aware bundle recommender system［C］// Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM， 2023：1198-1207.
［46］ YU D J， WANG X L， XIONG Y， et al. MHANER： A multi-source heterogeneous graph attention network for explainable recommendation in online games［J］. ACM Transactions on Intelligent Systems and Technology， 2023，15（4）：1-23.
［47］ JI H， ZHU J， SHI C Z， et al. Large-scale comb-k recommendation［C］// Proceedings of the Web Conference. ACM， 2021：2512-2523.
［48］ CHANG J X， GAO C， HE X N， et al. Bundle recommendation and generation with graph neural networks［J］. IEEE Transactions on Knowledge and Data Engineering， 2021， 35（3）：2326-2340.
［49］ HE R， PACKER C， MCAULEY J. Learning compatibility across categories for heterogeneous item recommendation［C］ // 2016 IEEE 16th International Conference on Data Mining （ICDM）. IEEE， 2016：937-942.
［50］ BLEI D M， NG A Y， JORDAN M I. Latent dirichlet allocation［J］. Journal of Machine Learning Research， 2003，3：993-1022.
［51］ MIKOLOV T， CHEN K， CORRADO G， et al. Efficient estimation of word representations in vector space［J］. arXiv preprint arXiv：1301.3781， 2013.
［52］ VASILEVA M I， PLUMMER B A， DUSAD K， et al. Learning type-aware embeddings for fashion compatibility［C］// Proceedings of the European Conference on Computer Vision （ECCV）. Springer， 2018：390-405.
［53］ LU Z， HU Y， JIANG Y C， et al. Learning binary code for personalized fashion recommendation［C］// Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE， 2019：10562-10570.
［54］ CHEN W， HUANG P P， XU J M， et al. POG： Personalized outfit generation for fashion recommendation at Alibaba iFashion［C］// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM， 2019：2662-2670.
［55］ LI M， LI L， XIE Q， et al. MealRec： A meal recommendation dataset［J］. arXiv preprint arXiv：2205.12133， 2022.
［56］ MCAULEY J， TARGETT C， SHI Q， et al. Image-based recommendations on styles and substitutes［C］// Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM， 2015：43-52.

[1]	ZHANG Jingying1, GENG Lin2, LIU Ningzhong2. Low-data Fine-grained Image Classification Based on Self-distillation and Self-attention Enhancement [J]. Computer and Modernization, 2025, 0(09): 27-34.
[2]	ZHU Haiyu1, SUN Xiaoyan1, YUAN Zhenming1, YANG Lijing2. Prediction of Breast Cancer Hospitalization Costs Based on Stacking Ensemble and Explainable Models [J]. Computer and Modernization, 2025, 0(09): 119-126.
[3]	WEI Huimin1, 2, ZHOU Jiake1, 2, WEN Yongjun1, 2, TANG Lijun1, 2. Chinese Entity Relation Joint Extraction Method Based on Deep Learning [J]. Computer and Modernization, 2025, 0(08): 10-15.
[4]	LI Jia. Classification and Diagnosis of Breast Cancer Histopathological Images Based on Transfer Learning [J]. Computer and Modernization, 2025, 0(08): 89-96.
[5]	DENG Chao1, 2, YANG Fengkun1, 2, LI Jun3, CHEN Liangliang1, 2, GUO Zhichong4. Complex Fault Diagnosis Method of Charging Facilities Based on Bi-LSTM Residual Network [J]. Computer and Modernization, 2025, 0(07): 90-96.
[6]	LAI Zhiyong, WANG Tinghua, ZHANG Xin. Feature Weighted Support Vector Machine Based on HSIC Lasso [J]. Computer and Modernization, 2025, 0(07): 119-126.
[7]	LIU Shaojun, SHA Yitian, JIN Qianqian, CHEN Peng, WU Yue. LDA-IDF Process Log Anomaly Detection Method for Smart Grids [J]. Computer and Modernization, 2025, 0(06): 114-119.
[8]	LI Zhuoqi, ZHAO Lihui. Image Encryption Method Based on Poisoning Attack Strategy [J]. Computer and Modernization, 2025, 0(05): 41-47.
[9]	WANG Dongfang1, YANG Yan1, ZHANG Dong1, HAN Wenrui2, LI Mingchang2. DSA De-artifacting Algorithm Based on Deformation Field Registration [J]. Computer and Modernization, 2025, 0(05): 86-90.
[10]	WEI Yunsong1, 2, LI Jiaqiang1, 2, HE Chao1, 2, 3, YU Haisheng1, 2, CHEN Yanlin1, 2, ZHAO Longqing1, 2, WEI Rongkun1, 2. Research Advances on 3D Object Detection Method Based on Visual Information and LiDAR for Intelligent Driving [J]. Computer and Modernization, 2025, 0(05): 91-102.
[11]	XU Ling1, ZHANG Dong1, WEN Shen1, HU Ping2. Glioma Segmentation and Classification Network Assisted by Object Detection [J]. Computer and Modernization, 2025, 0(05): 111-116.
[12]	ZHUANG Yu, FU Xiaojin, LI Sha, WU Zheng. UAV Small Target Detection Based on XMB-YOLOv5s [J]. Computer and Modernization, 2025, 0(04): 29-35.
[13]	LI Kai, JIN Yunpeng, LI Haiyang, KONG Shasha, YANG Peng, FANG Chengwu, HUANG Xiangjie, HAN Yaosheng, LI Chunmei. AGP Calculation Methods in UAV Imagery Based on Image Segmentation [J]. Computer and Modernization, 2025, 0(04): 83-88.
[14]	WANG Jiale, SONG Wenai, FU Lizhen. Occupational Pneumoconiosis Screening Based on HA-Net Model [J]. Computer and Modernization, 2025, 0(04): 103-110.
[15]	TANG Rui1, WU Jianchao1, CHEN Jianbo1, CHAI Jiang1, WANG Qian1, HE Yuchen2. Improved YOLOv8s Algorithm Based on GiraffeDet for Transmission Line Icing Detection [J]. Computer and Modernization, 2025, 0(03): 6-11.