Abstract
A recommendation system can help users to find relevant products or services that they might want to buy or consume. In most of the real-world applications, user’s long-term profiles may not exist for a large number of users, which might be the reason that they are visiting the website for the first time or they may not be logged in. The frequent change in user’s behavior requires a system which captures the present context or the short time behavior in real time. To predict the short-term interest of a user in an online session is a very relevant problem in practice. In this paper, we have applied eight machine learning models on the different datasets from different domains to check the performance of models and compared the results. From the obtained results, it is observed that the session-based KNN (SKNN) and its variants give promising results compared to the other’s methods.
Similar content being viewed by others
References
Adamczak J, Deldjoo Y, Moghaddam FB, Knees P, Leyson G-P, Monreal P (2020) Session-based hotel recommendations dataset: as part of the acm recommender system challenge 2019. ACM Trans Intell Syst Technol (TIST) 12(1):1–20
Balabanović M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):66–72
Bellini P, Nesi P, Palesi A, Pantaleo G (2021) Fashion retail recommendation system by multiple clustering. In: Proceedings of the 27th international DMS conference on visualization and visual languages, Pittsburgh, PA, USA, pp 29–30
Bellini P, Palesi LAI, Nesi P, Pantaleo G (2022) Multi clustering recommendation system for fashion retail. Multimed Tools Appl :1–28
Ben-Shimon D, Tsikinovsky A, Friedmann M, Shapira B, Rokach L, Hoerle J (2015) Recsys challenge 2015 and the yoochoose dataset. In: Proceedings of the 9th ACM conference on recommender systems, Vienna, Austria, pp 357–358
Bertin-Mahieux T, Ellis DP, Whitman B, Lamere P (2011) The million song dataset. In: ISMIR
Bonnin G, Jannach D (2014) Automated generation of music playlists: survey and experiments. ACM Comput Surv (CSUR) 47(2):1–35
Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on uncertainty in artificial intelligence, Madison, Wisconsin, pp 43–52
Chen S, Moore JL, Turnbull D, Joachims T (2012) Playlist prediction via metric embedding. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, Beijing, China, pp 714–722
Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next: Successive point-of-interest recommendation. In: Twenty-third international joint conference on artificial intelligence, Beijing, China
Gantz J, Reinsel D (2012) The digital universe in 2020: big data, bigger digital shadows, and biggest growth in the far east. IDC iView IDC Analyze Future 2007(2012):1–16
Garcin F, Zhou K, Faltings B, Schickel V (2012) Personalized news recommendation based on collaborative filtering. In: 2012 IEEE/WIC/ACM International conferences on web intelligence and intelligent agent technology, vol. 1, vol 1730. IEEE, NW Washington. DC, United States, pp 437–441
Garcin F, Dimitrakakis C, Faltings B (2013) Personalized news recommendation with context trees. In: Proceedings of the 7th ACM conference on recommender systems, Hong Kong, China, pp 105–112
Grbovic M, Radosavljevic V, Djuric N, Bhamidipati N, Savla J, Bhagwan V, Sharp D (2015) E-commerce in your inbox: product recommendations at scale. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, Sydney, NSW, Australia, pp 1809–181
Guo L, Yin H, Wang Q, Chen T, Zhou A, Quoc Viet Hung N (2019) Streaming session-based recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, anchorage, AK, USA, pp 1569–1577
Hariri N, Mobasher B, Burke R (2012) Context-aware music recommendation based on latenttopic sequential patterns. In: Proceedings of the sixth ACM conference on Recommender systems, Dublin, Ireland, pp 131–138
Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2015) Session-based recommendations with recurrent neural networks, arXiv:http://arxiv.org/abs/1511.06939
Hidasi B, Tikk D (2016) General factorization framework for context-aware recommendations. Data Min Knowl Disc 30(2):342–371
Hidasi B, Karatzoglou A (2018) Recurrent neural networks with top-k gains for session-based recommendations. In: Proceedings of the 27th ACM international conference on information and knowledge management, Torino, Italy, pp 843–852
Hu L, Cao L, Wang S, Xu G, Cao J, Gu Z (2017) Diversifying personalized recommendation with user-session context. In: International joint conferences on artificial intelligence organization, Melbourne, Australia, pp 1858–1864
Jannach D, Lerche L, Jugovac M (2015) Adaptation and evaluation of recommendations for short-term shopping goals. In: Proceedings of the 9th ACM conference on recommender systems, Vienna, Austria, pp 211–218
Jannach D, Ludewig M (2017) When recurrent neural networks meet the neighborhood for session-based recommendation. In: Proceedings of the Eleventh ACM conference on recommender systems, Como, Italy, pp 306–310
Kamehkhosh I, Jannach D, Ludewig M (2017) A comparison of frequent pattern techniques and a deep learning method for session-based recommendation. In: Workshop on temporal reasoning in recommender systems, Como, Italy, pp 50–56
Knees P, Deldjoo Y, Moghaddam FB, Adamczak J, Leyson G-P, Monreal P (2019) Recsys challenge 2019: session-based hotel recommendations. In: Proceedings of the 13th ACM conference on recommender systems, Copenhagen, Denmark, pp 570–571
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37
Li Y, Lu L, Xuefeng L (2005) A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in e-commerce. Expert Syst Appl 28(1):67–77
Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80
Liu S, Zheng Y (2020) Long-tail session-based recommendation. In: Fourteenth ACM conference on recommender systems, pp 509–514
Ludewig M, Jannach D (2018) Evaluation of session-based recommendation algorithms. User Model User-Adap Inter 28(4–5):331–390
Ludewig M, Mauro N, Latifi S, Jannach D (2021) Empirical analysis of session-based recommendation algorithms. User Model User-Adap Inter 31(1):149–181
Mobasher B, Dai H, Luo T, Nakagawa M (2002) Using sequential and non-sequential patterns in predictive web usage mining tasks. In: Proceedings 2002 IEEE international conference on data mining, vol 2002. IEEE, Japan, pp 669–672
Mooney RJ, Roy L (2000) Content-based book recommending using learning for text categorization. In: Proceedings of the fifth ACM conference on Digital libraries, San Antonio, Texas, USA, pp 195–204
Quadrana M, Cremonesi P, Jannach D (2018) Sequence-aware recommender systems. ACM Comput Surv (CSUR) 51(4):1–36
Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, Hong Kong, pp 285–295
Shani G, Heckerman D, Brafman RI, Boutilier C (2005) An mdp-based recommender system. J Mach Learn Res 6(43):1265–1295
Sharma R, Gopalani D, Meena Y (2017) Collaborative filtering-based recommender system: approaches and research challenges. In: 2017 3rd international conference on computational intelligence & communication technology (CICT). IEEE, India, pp 1–6
Tan YK, Xu X, Liu Y (2016) Improved recurrent neural networks for session-based recommendations. In: Proceedings of the 1st workshop on deep learning for recommender systems, Boston, MA, USA, pp 17–22
Tavakol M, Brefeld U (2014) Factored mdps for detecting topics of user sessions. In: Proceedings of the 8th ACM conference on recommender systems, Foster City, Silicon Valley, California, USA, pp 33–40
Turrin R, Quadrana M, Condorelli A, Pagano R, Cremonesi P (2015) 30 music listening and playlists dataset. In: Recsys posters, Vienna, Austria
Verstrepen K, Goethals B (2014) Unifying nearest neighbors collaborative filtering. In: Proceedings of the 8th ACM conference on recommender systems, Foster City, Silicon Valley, California, USA, pp 177–184
Wang S, Cao L, Wang Y, Sheng QZ, Orgun MA, Lian D (2021) A survey on session-based recommender systems. ACM Comput Surv (CSUR) 54 (7):1–38
Wang N, Wang S, Wang Y, Sheng QZ, Orgun MA (2022) Exploiting intra-and inter-session dependencies for session-based recommendations. World Wide Web 25(1):425–443
Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T (2019) Session-based recommendation with graph neural networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 33. Hilton Hawaiian Village, Honolulu, Hawaii, USA, pp 346–353
Acknowledgements
The authors are grateful to the reviewers for their careful reviews and highly helpful comments. The authors are also grateful to the Seed project, funded by TEQIP-III, NIT Patna, the Department of Computer Science and Engineering, NIT Patna, for providing all facilities and guidance.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interests
All authors certify that they have no conflict of interests/competing interests in the subject matter or materials discussed in this manuscript.
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 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
Kumar, C., Kumar, M. User session interaction-based recommendation system using various machine learning techniques. Multimed Tools Appl 82, 21279–21309 (2023). https://doi.org/10.1007/s11042-022-13993-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-13993-8