Evaluating Session-Based Recommendation Approaches on Datasets from Different Domains-Reference-Cited by-同舟云学术

Evaluating Session-Based Recommendation Approaches on Datasets from Different Domains

Published:2019 Issue: Volume: Page:577-592
ISSN:0302-9743
Container-title:Future Data and Security Engineering
language:
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Author:

Dang Tran Khanh,Nguyen Quang Phu,Nguyen Van Sinh

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-35653-8_37

Reference21 articles.

1. Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: ICLR (2016)

2. Quadrana, M., Cremonesi, P., Jannach, D.: Sequence-aware recommender systems. Comput. Surveys 54(1), 1–36 (2018)

3. Robin, D., Hugues, B.: Collaborative filtering with recurrent neural networks. CoRR, abs/1608.07400 (2016)

4. Hidasi, B., Karatzoglou, A.: Recurrent neural networks with top-k gains for session-based recommendations. CoRR abs/1706.03847 (2017). arXiv:1706

5. Kamehkhosh, I., Jannach, D., Ludewig, M.: A comparison of frequent pattern techniques and a deep learning method for session-based recommendation. In: TempRec Workshop at ACM RecSys 2017, Como, Italy (2017)

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