Abstract
Many approaches based on Graph Neural Networks (GNNs) have been proposed to identify relationships between users and items while modelling user preferences with significant improvements in recommendation quality. Besides accuracy, diversity in recommendation is often a desirable property for a better user experience in a real-world application. Recently many recommendation techniques based on heterogeneous information networks have been drawing attention to improvement in diversity. However, most such algorithms use re-ranking approaches or diversity regularization (ensemble learning) in a heterogeneous graph network. These approaches often compromise with accuracy to include diversity in the recommendation. The author proposed a novel technique involving both diversity and accuracy at the same time for recommendation generation. Our approach uses implicit user information to generate a low-dimensional embedding representation for each node. The model also includes derived user features for diversity to train the model for diversified recommendation generation. The proposed model iteratively finds infrequently recommended yet relevant items, adds them to the users’ final recommendation lists, and balances the accuracy diversity tradeoff. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed model, Diverse Heterogeneous Node Embedding Model for Recommendation (Div-HetNEMRec), for diverse recommendations with substantially better coverage and reasonably good improvement in accuracy over the state-of-the-art techniques.
Similar content being viewed by others
Dataset Availability
The datasets used for the experimental evaluation of the proposed approach are publicly available.
References
Abdollahpouri H, Robin B, Bamshad M (2019) Managing popularity bias in recommender systems with personalized re-ranking. arXiv preprint arXiv:1901.07555
Adomavicius G, YoungOk K (2011) Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering 24(5):896–911
Anderson A, et al (2020) Algorithmic effects on the diversity of consumption on spotify. Proceedings of The Web Conference 2020
Aytekin T, Mahmut Özge K (2014) Clustering-based diversity improvement in top-N recommendation. J Intell Inf Syst 42(1):1–18
Aytekin T, Mahmut Özge K (2014) Clustering-based diversity improvement in top-N recommendation. J Intell Inf Sys 42(1):1–18
Bao Y, Hui F, Jie Z (2014) Topicmf: Simultaneously exploiting ratings and reviews for recommendation. Twenty-Eighth AAAI conference on artificial intelligence
Bradley K, Barry S (2001) Improving recommendation diversity. Proceedings of the Twelfth Irish Conference on Artificial Intelligence and Cognitive Science, Maynooth, Ireland. vol 85 (94)
Çanakoğlu E, İbrahim M, Tevfik A (2021) Integrating Individual and Aggregate Diversity in Top-N Recommendation. INFORMS Journal on Computing 33(1):300–318
Cao S, Wei L, Qiongkai X (2015) Grarep: Learning graph representations with global structural information. Proceedings of the 24th ACM international on conference on information and knowledge management
Chen C, et al (2021) Graph Heterogeneous Multi-Relational Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence 35(5)
Darban ZZ, Mohammad HV (2022) GHRS: Graph-based hybrid recommendation system with application to movie recommendation. Expert Systems with Applications 200
Di Noia T et al (2017) Adaptive multi-attribute diversity for recommender systems. Information Sciences 382:234–253
Dong Y, Nitesh V C, Ananthram S (2017) metapath2vec: Scalable representation learning for heterogeneous networks. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Ekstrand MD, et al (2014) User perception of differences in recommender algorithms. Proceedings of the 8th ACM Conference on Recommender systems
Fan S, et al (2019) Metapath-guided heterogeneous graph neural network for intent recommendation. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Feng W, Jianyong W (2012) Incorporating heterogeneous information for personalized tag recommendation in social tagging systems. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Fu X et al (2020) MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding. Proceedings of The Web Conference 2020
Gao Z, et al (2022) Mitigating the Filter Bubble while Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Grover A, Jure L (0216) node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining
Grover A, Jure L (2016) node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Gu L, Peng Y, Yongqiang D (2017) Diversity optimization for recommendation using improved cover tree. Knowledge-Based Systems 135:1–8
Hamilton W L, Rex Y, Jure L (2017) Inductive representation learning on large graphs. arXiv preprint arXiv:1706.02216
He X, Deng K, Wang X, Li Y, Zhang Y, Wang M. (2020) 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) July
Helberger N, Kari K, Lucia D (2018) Exposure diversity as a design principle for recommender systems. Information, Communication & Society 21(2):191–207
Isufi E, Matteo P, Alan H (2021) Accuracy-diversity trade-off in recommender systems via graph convolutions. Information Processing & Management 58(2):102459
Jacobson K, et al (2016) Music personalization at Spotify. Proceedings of the 10th ACM Conference on Recommender Systems
Javari A, Mahdi J (2015) A probabilistic model to resolve diversity-accuracy challenge of recommendation systems. Knowledge and Information Systems 44(3):609–627
Jeong J, et al (2020) div2vec: Diversity-Emphasized Node Embedding. arXiv preprint arXiv:2009.09588
Jin J, et al (2020) An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Kunaver M, Tomaž P (2017) Diversity in recommender systems-A survey. Knowledge-based systems 123:154–162
Liang D, et al (2018) Variational autoencoders for collaborative filtering. Proceedings of the 2018 world wide web conference
Li C, et al (2021) Sequence-aware Heterogeneous Graph Neural Collaborative Filtering. Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics
Linden G, Brent S, Jeremy Y (2003) Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing 7(1): 76–80
Logesh R et al (2020) Hybrid bio-inspired user clustering for the generation of diversified recommendations. Neural Computing and Applications 32(7):2487–2506
Nandanwar S, Aayush M, Narasimha MM (2018) Fusing diversity in recommendations in heterogeneous information networks. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
Pal S, et al (2016) Deep learning for network analysis: Problems, approaches and challenges. MILCOM 2016-2016 IEEE Military Communications Conference. IEEE
Panteli A, Basilis B (2023) Improvement of similarity-diversity trade-off in recommender systems based on a facility location model. Neural Computing and Applications 35(1):177–189
Paudel B, Abraham B (2021) Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks. arXiv preprint arXiv:2102.09635
Perozzi B, Rami A-R, Steven S (2014) Deepwalk: Online learning of social representations. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
Rendle S, et al (2012) BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618
Shi C et al (2018) Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng 31(2):57–70
Sun J, et al (2020) A framework for recommending accurate and diverse items using bayesian graph convolutional neural networks. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Su Y, Zhang R, M Erfani S, Gan J (2021) Neural graph matching based collaborative filtering. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 849–858). July
Tintarev N, Matt D, Judith M (2013) Adapting recommendation diversity to openness to experience: A study of human behaviour. International Conference on User Modeling, Adaptation, and Personalization. Springer, Berlin, Heidelberg
Vargas S, Pablo C, David V (2011) Intent-oriented diversity in recommender systems. Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Wang J, Arjen P. De Vries, Marcel JT R (2006) Unifying user-based and item-based collaborative filtering approaches by similarity fusion. Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Xia H, Kai H, Yuan L (2022) Unexpected interest recommender system with graph neural network. Complex & Intelligent Systems 1–15
Xie R, et al (2021) Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network. arXiv preprint arXiv:2102.03787
Yadav N et al (2022) Clus-DR: Cluster-based pre-trained model for diverse recommendation generation. Journal of King Saud University-Computer and Information Sciences 34(8):6385–6399
Yadav N, Singh Anil Kumar, Sukomal P (2022) Improved self-attentive Musical Instrument Digital Interface content-based music recommendation system. Computational Intelligence 38(4):1232–1257
Yadav N, Anil Kumar Singh (2020) Bi-directional encoder representation of transformer model for sequential music recommender system. Forum for Information Retrieval Evaluation
Yadav N, et al (2021) Diversity in recommendation system: A cluster based approach. Hybrid Intelligent Systems: 19th International Conference on Hybrid Intelligent Systems (HIS 2019) held in Bhopal, India, 19. Springer International Publishing. 10-12 Dec 2019
Yan X et al (2020) Synergetic information bottleneck for joint multi-view and ensemble clustering. Information Fusion 56:15–27
Yan S et al (2021) Attention-aware metapath-based network embedding for HIN based recommendation. Expert Systems with Applications 174:114601
Yan X, et al (2020) Heterogeneous dual-task clustering with visual-textual information. Proceedings of the 2020 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics
Yang C et al (2021) Network Embedding: Theories, Methods, and Applications. Synthesis Lectures on Artificial Intelligence and Machine Learning 15(2):1–242
Yang L, et al (2023) DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation. Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
Zheng Y, et al (2021) Dgcn: Diversified recommendation with graph convolutional networks. Proceedings of the Web Conference 2021
Zhu X, et al (2007) Improving diversity in ranking using absorbing random walks. Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Yadav, N., Pal, S. & Singh, A.K. Diversified recommendation using implicit content node embedding in heterogeneous information network. Multimed Tools Appl 83, 20605–20635 (2024). https://doi.org/10.1007/s11042-023-16135-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-16135-w