Service recommendation systems play a crucial role in delivering personalized user experiences across various domains. However, capturing the heterophily patterns and the multi-dimensional nature of user-service item interactions poses significant challenges. To address these challenges, we propose a service recommendation model named GNN (Hypergraph-based Heterophily-Aware Neural Network for Service Recommendation), which incorporates three key components: (1) a hypergraph disentangled contrast module that explicitly separates homophily and heterophily signals within hyperedges, enabling more accurate feature representation; (2) a heterophily-aware attention mechanism that dynamically adjusts information propagation weights based on node feature differences, enhancing interaction efficiency between heterophilic nodes; and (3) a dynamic multi-interest learning module that disentangles users’ latent preferences into multiple interest vectors and activates relevant interests according to target service items, achieving fine-grained modeling of cross-category preferences. Extensive experiments on Steam, MovieLens, and Yelp datasets demonstrate the effectiveness of GNN, with its Recall@20 metric outperforming the state-of-the-art baseline (HMGSR) by approximately 14.1% on Steam, 12.0% on MovieLens, and 13.9% on Yelp—and is consistently superior to the latest models across all datasets.
HuangCXuHXuY, et al.Knowledge-aware coupled graph neural network for social recommendation. In Proceedings of the AAAI conference on artificial intelligence, volume 35. pp. 4115–4122.
2.
LiuYLiuQTianY, et al.Concept-aware denoising graph neural network for micro-video recommendation. In Proceedings of the 30th ACM international conference on information & knowledge management. pp. 1099–1108.
3.
HuangCWuXZhangX, et al.Online purchase prediction via multi-scale modeling of behavior dynamics. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. pp. 2613–2622.
4.
HuangLMaYLiuY, et al.Position-enhanced and time-aware graph convolutional network for sequential recommendations. ACM Transactions on Information Systems2023; 41: 1–32.
5.
ZhangMWuSYuX, et al.Dynamic graph neural networks for sequential recommendation. IEEE Trans Knowl Data Eng2022; 35: 4741–4753.
6.
YingRHeRChenK, et al.Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. pp. 974–983.
7.
HeXDengKWangX, et al.Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. pp. 639–648.
8.
MaoKZhuJXiaoX, et al.Ultragcn: ultra simplification of graph convolutional networks for recommendation. In Proceedings of the 30th ACM international conference on information & knowledge management. pp. 1253–1262.
9.
YuJYinHLiJ, et al.Self-supervised multi-channel hypergraph convolutional network for social recommendation. In Proceedings of the web conference 2021. pp. 413–424.
10.
XiaLHuangCXuY, et al.Hypergraph contrastive collaborative filtering. In Proceedings of the 45th International ACM SIGIR conference on research and development in information retrieval. pp. 70–79.
11.
KhanBWuJYangJ, et al.Heterogeneous hypergraph neural network for social recommendation using attention network. ACM Transactions on Recommender Systems2025; 3: 1–22.
12.
JiSFengYJiR, et al.Dual channel hypergraph collaborative filtering. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. pp. 2020–2029.
13.
XiaLHuangCZhangC. Self-supervised hypergraph transformer for recommender systems. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. pp. 2100–2109.
14.
ZhengXWangYLiuY, et al.Graph neural networks for graphs with heterophily: A survey. arXiv preprint arXiv:220207082 2022.
15.
LuanSHuaCLuQ, et al.The heterophilic graph learning handbook: Benchmarks, models, theoretical analysis, applications and challenges. arXiv preprint arXiv:240709618 2024.
16.
LiJWeiZDanJ, et al.Heteronet: Heterophily-aware representation learning on heterogenerous graphs. arXiv preprint arXiv:231011664 2023.
17.
WangXJiHShiC, et al.Heterogeneous graph attention network. In The world wide web conference. pp. 2022–2032.
18.
FengYYouHZhangZ, et al.Hypergraph neural networks. In Proceedings of the AAAI conference on artificial intelligence, volume 33. pp. 3558–3565.
19.
YinNFengFLuoZ, et al.Dynamic hypergraph convolutional network. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, pp. 1621–1634.
20.
KorenYBellRVolinskyC. Matrix factorization techniques for recommender systems. Computer2009; 42: 30–37.
HuYKorenYVolinskyC. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE international conference on data mining. Ieee, pp. 263–272.
23.
SarwarBKarypisGKonstanJ, et al.Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web. pp. 285–295.
WangXHeXWangM, et al.Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval. pp. 165–174.
26.
LvMLiuXXuY. Dynamic multi-interest graph neural network for session-based recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39. pp. 12328–12336.
27.
CenYZhangJZouX, et al.Controllable multi-interest framework for recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. pp. 2942–2951.
28.
ZhangWBeiYYangL, et al.Cold-start recommendation towards the era of large language models (llms): A comprehensive survey and roadmap. arXiv preprint arXiv:250101945 2025.
29.
VolkovsMYuGPoutanenT. Dropoutnet: Addressing cold start in recommender systems. Adv Neural Inf Process Syst2017; 30: 4964–4973.
30.
LiJJingMLuK, et al.From zero-shot learning to cold-start recommendation. In Proceedings of the AAAI conference on artificial intelligence, volume 33. pp. 4189–4196.
31.
LeeHImJJangS, et al.Melu: Meta-learned user preference estimator for cold-start recommendation. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. pp. 1073–1082.
32.
KimJKimEYeoK, et al.Content-based graph reconstruction for cold-start item recommendation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 1263–1273.
33.
WangWChenBLiuB, et al.Mutual information assisted graph convolution network for cold-start recommendation. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 6785–6789.
34.
CaoYHuSGongY, et al.Gift: Graph-guided feature transfer for cold-start video click-through rate prediction. In Proceedings of the 31st ACM international conference on information & knowledge management. pp. 2964–2973.
35.
BiYSongLYaoM, et al.Dcdir: A deep cross-domain recommendation system for cold start users in insurance domain. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. pp. 1661–1664.
36.
XieYYuCJinX, et al.Heterogeneous graph contrastive learning for cold start cross-domain recommendation. Knowl Based Syst2024; 299: 112054.
37.
CaoJLiSYuB, et al.Towards universal cross-domain recommendation. In Proceedings of the Sixteenth ACM International Conference on web search and data mining. pp. 78–86.
38.
WuXZhouHShiY, et al.Could small language models serve as recommenders? towards data-centric cold-start recommendation. In Proceedings of the ACM Web Conference 2024. pp. 3566–3575.
39.
BaoKZhangJZhangY, et al.Tallrec: An effective and efficient tuning framework to align large language model with recommendation. In Proceedings of the 17th ACM conference on recommender systems. pp. 1007–1014.
40.
HuangFYangZJiangJ, et al.Large language model simulator for cold-start item recommendation. In: Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, 2025, pp.261–270.
41.
ZhouHZhouXZengZ, et al.A comprehensive survey on multimodal recommender systems: Taxonomy, evaluation, and future directions. arXiv preprint arXiv:230204473 2023.
42.
XuJChenZYangS, et al.A survey on multimodal recommender systems: Recent advances and future directions. arXiv preprint arXiv:250215711 2025.
VeličkovićPCucurullGCasanovaA, et al.Graph attention networks. arXiv preprint arXiv:171010903 2017.
45.
O’NeillMVaziripourEWuJ, et al.Condensing steam: Distilling the diversity of gamer behavior. In Proceedings of the 2016 internet measurement conference. pp. 81–95.
46.
JiangWGaoXXuG, et al.Challenging low homophily in social recommendation. In Proceedings of the ACM Web Conference 2024. pp. 3476–3484.
47.
SakongDNguyenTT. Handling heterophily in recommender systems with wavelet hypergraph diffusion. arXiv preprint arXiv:250114399 2025.
48.
WangZWeiWCongG, et al.Global context enhanced graph neural networks for session-based recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. pp. 169–178.
49.
ZhengXWangGZhangJ, et al.H-mgsr: a hierarchical motif-based graph attention neural network for service recommendation. In 2023 IEEE international conference on web services (ICWS). IEEE, pp. 553–562.
