Abstract
To improve the performance of recommender systems in a practical manner, many hybrid recommendation approaches have been proposed. Recently, some researchers apply the idea of ranking to recommender systems which yield plausible results. Collaborative ranking is a popular ranking based method, it regards that unrated items have lower rankings than rated items for a user. Unfortunately, the existing collaborative ranking approaches only focus on the partial associations with users and items, and thus fail to detect some features that could potentially improve the performance of the recommender systems. For this reason, these methods continue to suffer from data sparsity and do not work well for recommending an interesting item to an individual user. To address these issues, we present an Assembled Collaborative Ranking with Random Walk (ACR-RW) approach based on the combination of collaborative ranking and random walk method, which can be used to rank items according to expected user preferences by detecting both absolute and relative correlative information, in order to recommend top-ranked items to potentially interested users. On the basis of ACR-RW, we can improve the collaborative ranking approaches by adding absolute relationship information and defining the partial order relationship in assemblages rather than the global, so as to better describe and predict one user’s preference. Finally, we implement experiments on three real-world datasets, and the results show that our approach consistently outperforms all other comparative approaches, demonstrating its effectiveness for recommendation tasks.
Similar content being viewed by others
References
Balakrishnan S, Chopra S (2012) Collaborative ranking. In: Proceedings of the fifth ACM international conference on Web search and data mining, pp 143–152
Chen J, Zhang H, He X, Nie L, Liu W, Chua T-S (2017) Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In: Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, pp 335–344
Christakopoulou K, Banerjee A (2015) Collaborative ranking with a push at the top. In: Gangemi A, Leonardi S, Panconesi A (eds) Proceedings of the 24th International Conference on World Wide Web, WWW 2015, Florence, Italy, May 18-22, 2015, ACM, pp 205–215
Feng Y, Lv F, Hu B, Sun F, Kuang K, Liu Y, Liu Q, Ou W (2020) Mtbrn: Multiplex target-behavior relation enhanced network for click-through rate prediction. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp 2421–2428
Feng S, Zhang H, Wang L, Liu L, Xu Y (2019) Detecting the latent associations hidden in multi-source information for better group recommendation. Knowl-Based Syst 171:56–68
Guo G, Zhang J, Sun Z, Yorke-Smith N (2015) Librec: A java library for recommender systems. In: Posters, demos, late-breaking results and workshop proceedings of the 23rd conference on user modelling, Adapt. Personalization
Hazrati N, Shams B, Haratizadeh S (2019) Entity representation for pairwise collaborative ranking using restricted boltzmann machine. Expert Syst Appl 116:161–171
He X, Zhang H, Kan M-Y, Chua T-S (2016) Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, pp 549–558
He X, Zhang H, Kan M, Chua T (2020) Fast matrix factorization for online recommendation with implicit feedback, arXiv:1708.05024
Huang C, Gan Z, Ye F, Wang P, Zhang M (2020) Kncr: Knowledge-Aware neural collaborative ranking for recommender systems. In: 2020 IEEE Intl conf on dependable, autonomic and secure computing, intl conf on pervasive intelligence and computing, intl conf on cloud and big data computing, intl conf on cyber science and technology congress, DASC/PiCom/CBDCom/CyberSciTech. IEEE, pp 339–344
Hwang T, Park C, Hong J, Kim SK (2016) An algorithm for movie classification and recommendation using genre correlation. Multimed Tools Appl 75(20):12843–12858
Jin R, Chai JY, Si L (2004) An automatic weighting scheme for collaborative filtering. In: Sanderson M, Järvelin K, Allan J, Bruza P (eds) SIGIR 2004: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Sheffield, UK, July 25-29, 2004. ACM, pp 337–344
Koren Y, Bell RM, Volinsky C (2009) Matrix factorization techniques for recommender systems. IEEE Comput 42(8):30–37
Kouadria A, Nouali O, Al-Shamri MYH (2020) A multi-criteria collaborative filtering recommender system using learning-to-rank and rank aggregation. Arab J Sci Eng 45(4):2835–2845
Lee J, Bengio S, Kim S, Lebanon G, Singer Y (2014) Local collaborative ranking. In: Chung C, Broder AZ, Shim K, Suel T (eds) 23Rd international world wide web conference, WWW ’14, seoul, republic of korea, april 7-11, 2014. ACM, pp 85–96
Lee J, Lee D, Lee Y, Hwang W, Kim S (2016) Improving the accuracy of top-n recommendation using a preference model. Inf Sci 348:290–304
Li G, Ou W (2016) Pairwise probabilistic matrix factorization for implicit feedback collaborative filtering. Neurocomputing 204:17–25
Li X, Yang F, Ma Y, Ma H (2020) Multi-label classification of short text based on similarity graph and restart random walk model. In: International Conference on Intelligent Information Processing. Springer, pp 67–77
Liang D, Altosaar J, Charlin L, M Blei D (2016) Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence. In: Proceedings of the 10th ACM conference on recommender systems. ACM, pp 59–66
Liu W, Lai H, Wang J, Ke G, Yang W, Yin J (2020) Mix geographical information into local collaborative ranking for poi recommendation. World Wide Web 23(1):131–152
Liu S, Wang B, Xu M (2017) Event recommendation based on graph random walking and history preference reranking. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 861–864
M Elkahky A, Song Y, He X (2015) A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, pp 278–288
Manju G, Abhinaya P, Hemalatha M, Manju G et al (2020) Cold start problem alleviation in a research paper recommendation system using the random walk approach on a heterogeneous user-paper graph. International Journal of Intelligent Information Technologies (IJIIT) 16(2):24–48
Pan W, Chen L (2013) GBPR: Group preference based bayesian personalized ranking for one-class collaborative filtering. In: Rossi F (ed) IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 3-9, 2013, IJCAI/AAAI, pp 2691–2697
Pan W, Zhong H, Xu C, Ming Z (2015) Adaptive bayesian personalized ranking for heterogeneous implicit feedbacks. Knowl-Based Syst 73:173–180
Park H, Jung J, Kang U (2017) A comparative study of matrix factorization and random walk with restart in recommender systems. In: 2017 IEEE International conference on big data, bigdata 2017, boston, MA, USA, December 11-14, pp 756–765
Park D, Neeman J, Zhang J, Sanghavi S, Dhillon IS (2015) Preference completion: Large-scale collaborative ranking from pairwise comparisons. In: Bach FR, Blei DM (eds) Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, Vol. 37 of JMLR Workshop and Conference Proceedings, JMLR.org, pp 1907–1916
Peña FJ, O’Reilly-Morgan D, Tragos EZ, Hurley N, Duriakova E, Smyth B, Lawlor A (2020) Combining rating and review data by initializing latent factor models with topic models for top-n recommendation. In: Fourteenth ACM Conference on Recommender Systems, pp 438–443
Rafailidis D, Crestani F (2016) Collaborative ranking with social relationships for top-n recommendations. In: Perego R, Sebastiani F, Aslam JA, Ruthven I, Zobel J (eds) Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016. ACM, pp 785–788
Rendle S, Freudenthaler C, Gantner Z, Schmidt-thieme L (2009) BPR: bayesian personalized ranking from implicit feedback. In: Bilmes JA, Ng AY (eds) UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18-21, 2009. AUAI Press, pp 452–461
Rudin C (2009) The p-norm push: a simple convex ranking algorithm that concentrates at the top of the list. J Mach Learn Res 10:2233–2271
Shao Y, Huang S, Miao X, Cui B, Chen L (2020) Memory-aware framework for efficient second-order random walk on large graphs. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp 1797–1812
Shi Y, Karatzoglou A, Baltrunas L, Larson M, Oliver N, Hanjalic A (2012) Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering. In: Cunningham P, Hurley NJ, Guy I, Anand SS (eds) Sixth ACM conference on recommender systems, recsys ’12, dublin, ireland, september 9-13, 2012. ACM, pp 139–146
Song B, Yang X, Cao Y, Xu C (2018) Neural collaborative ranking. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp 1353–1362
Tian D, Shi Z (2020) A two-stage hybrid probabilistic topic model for refining image annotation. International Journal of Machine Learning and Cybernetics 11(2):417–431
Tong H, Faloutsos C, Pan J (2006) Fast random walk with restart and its applications. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 18-22 December, vol 2006. Hong Kong, China, pp 613–622
Vahedian F, Burke R, Mobasher B (2017) Weighted random walk sampling for multi-relational recommendation. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. ACM, pp 230–237
Volkovs M, Zemel R (2012) Collaborative ranking with 17 parameters. Advances in Neural Information Processing Systems 25:2294–2302
Wang C, Zhu H, Zhu C, Qin C, Xiong H (2020) Setrank: a setwise bayesian approach for collaborative ranking from implicit feedback. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 6127–6136
Weimer M, Karatzoglou A, Le QV, Smola AJ (2007) COFI RANK - Maximum margin matrix factorization for collaborative ranking. In: Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December, vol 3-6, pp 1593–1600
Wu L, Hsieh C-J, Sharpnack J (2018) Sql-rank: A listwise approach to collaborative ranking. In: International Conference on Machine Learning, PMLR, pp 5315–5324
Wu L, Hsieh C, Sharpnack J (2018) Sql-rank: a listwise approach to collaborative ranking. In: Dy JG, Krause A (eds) Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, Vol. 80 of Proceedings of Machine Learning Research, PMLR, pp 5311–5320
Xu B, Lin H, Lin Y, Guan Y (2020) Integrating social annotations into topic models for personalized document retrieval. Soft Comput 24(3):1707–1716
Yang J, Chen C, Wang C, Tsai M (2018) Hop-rec: high-order proximity for implicit recommendation. In: Pera S, Ekstrand MD, Amatriain X, O’Donovan J (eds) Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, Vancouver, BC, Canada, October 2-7, 2018. ACM, pp 140–144
Yatnalkar G, Narman HS, Malik H (2020) An enhanced ride sharing model based on human characteristics and machine learning recommender system. Procedia Computer Science 170:626–633
Yu M, Quan T, Peng Q, Yu X, Liu L (2021) A model-based collaborate filtering algorithm based on stacked autoencoder. Neural Comput & Applic 1–9
Zheng L, Noroozi V, S Yu P (2017) Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM, pp 425–434
Zheng L, Tianlong Z, Huijian H, Caiming Z (2020) Personalized tag recommendation based on convolution feature and weighted random walk. Int J Comput Intell Syst 13(1):24–35
Acknowledgements
This work was supported in part by Taishan Scholar Project of Shandong of China, and the National Natural Science Foundation of China under Grant U1836216.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Jiang, R., Feng, S., Zhang, S. et al. A personalized recommendation method based on collaborative ranking with random walk. Multimed Tools Appl 81, 7345–7363 (2022). https://doi.org/10.1007/s11042-022-11980-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-11980-7