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A Study of Deep Learning-Based Approaches for Session-Based Recommendation Systems

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Abstract

Recommending relevant items of interest for a user is the main purpose of the recommendation system. In the past, those systems achieve the recommended list based on long-term user profiles. However, personal data privacy is becoming a big challenge recently. Thus, the recommendation system needs to reduce the dependence on user profiles while preserving high accuracy on the recommendation. Session-based recommendation is a recently proposed approach for the recommendation system to overcome the issue of user profiles dependency. The relevance of the problem is quite high and has triggered interest among researchers in observing the activities of users. It increased several proposals for session-based recommendation algorithms that aim to predict the next actions. In this paper, we would like to compare the performance of such algorithms by using various datasets and evaluation metrics. A deep learning approach named GRU4REC (Hidasi et al. in Session-based recommendations with recurrent neural networks, 2015) and simpler methods are included in our comparison. Real-world datasets from three different domains are included in our experiment. Our experiments reveal that in some cases of numerous unpopular items dataset, GRU4REC’s performance is lower than expected. However, its performance is significantly increased after applying our proposed sampling method. Therefore, our obtained results suggested that there is still room for improving deep learning session-based recommendation algorithms.

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Notes

  1. https://2015.recsyschallenge.com/challenge.html.

  2. https://cikm2016.cs.iupui.edu/cikm-cup/.

  3. https://ijcai-15.org/index.php/repeat-buyers-prediction-competition.

  4. https://www.kaggle.com/gspmoreira/news-portal-user-interactions-by-globocom.

  5. http://ijcai-16.org/.

  6. https://tianchi.aliyun.com/dataset/dataDetail?dataId=649.

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Acknowledgements

This research is supported by a project with the Department of Science and Technology, Ho Chi Minh City, Vietnam (contract with HCMUT No. 42/2019/HĐ-QPTKHCN, dated 11/7/2019). We also would like to thank the FDSE 2019 organizing committee and paper reviewers for suggesting corrections and improvements. The audience at our presentation session in FDSE 2019 conference also offered constructive feedbacks for the paper.

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Authors and Affiliations

  1. Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. International University, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam

    Quang Phu Nguyen & Van Sinh Nguyen

  3. Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam

    Tran Khanh Dang, Quang Phu Nguyen & Van Sinh Nguyen

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  1. Tran Khanh Dang
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  2. Quang Phu Nguyen
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Correspondence to Tran Khanh Dang.

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This article is part of the topical collection “Future Data and Security Engineering 2019” guest edited by Tran Khanh Dang.

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Dang, T.K., Nguyen, Q.P. & Nguyen, V.S. A Study of Deep Learning-Based Approaches for Session-Based Recommendation Systems. SN COMPUT. SCI. 1, 216 (2020). https://doi.org/10.1007/s42979-020-00222-y

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