Multi-feature Fusion Based Short Session Recommendation Model

被引：0
|
作者：
Xia H. [1 ,2 ]
Huang K. [1 ]
Liu Y. [1 ,2 ]
机构：
[1] School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi
[2] Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2023年 / 36卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Auxiliary Information; Feature Fusion; Session-Based Recommendation; Short Session;
D O I：
10.16451/j.cnki.issn1003-6059.202304005
中图分类号：
学科分类号：
摘要：
Most research on session recommendation systems focuses on long session recommendation and neglects short sessions. However, in practice short session information account for majority of the information. Due to the limited information contained in short sessions, it is crucial to learn more diverse user preferences and find similar context sessions accurately from short sessions. Therefore, a multi-feature fusion based short session recommendation model (MFFSSR) is proposed. Firstly, the node features and sequence features of sessions are learned respectively via neighborhood aggregation and recurrent neural networks. Secondly, the custom similarity calculation formula is utilized to retrieve the current user history session and other user sessions as context information, which alleviate the lack of information in short sessions. Next, the location-aware multi-head self-attention network is applied to fully explore the hidden features of sessions. Finally, the model recommends the next item based on the current session of multi-feature fusion. Experiments on two real datasets show that the proposed model is superior in terms of metrics. The code for the proposed model can be found at http://github com/ScarletHK/MFF-SRR. © 2023 Journal of Pattern Recognition and Artificial Intelligence. All rights reserved.
引用
收藏
页码：354 / 365
页数：11
相关论文
共 28 条
  • [1] AGGARWAL C C., Recommender Systems: The Textbook, (2016)
  • [2] EKSTRAND M D, RIEDL J T, KONSTAN J A., Collaborative Filtering Recommender Systems, in Human-Computer Interaction, 4, 2, pp. 81-173, (2011)
  • [3] WANG S J, CAO L B, WANG Y, Et al., A Survey on Session-Based Recommender Systems, ACM Computing Surveys, 54, 7, (2021)
  • [4] LIPTON Z C, BERKOWITZ J, ELKAN C., A Critical Review of Recurrent Neural Networks for Sequence Learning
  • [5] WANG S J, HU L, WANG Y, Et al., Sequential Recommender Systems: Challenges, Progress and Prospects [C/OL]
  • [6] LIU Q, ZENG Y F, MOKHOSI R, Et al., STAMP: Short-Term Attention/Memory Priority Model for Session-Based Recommendation, Proc of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1831-1839, (2018)
  • [7] WU S, TANG Y Y, ZHU Y Q, Et al., Session-Based Recommendation with Graph Neural Networks, Proc of the 33rd AAAI Conference on Artificial Intelligence and 31st Innovative Applications of Artificial Intelligence Conference and 9th AAAI Symposium on Educational Advances in Artificial Intelligence, pp. 346-353, (2019)
  • [8] LAI S Q, MENG E L, ZHANG F, Et al., An Attribute-Driven Mirror Graph Network for Session-Based Recommendation, Proc of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1674-1683, (2022)
  • [9] QUADRANA M, KARATZOGLOU A, HIDASI B, Et al., Personalizing Session-Based Recommendations with Hierarchical Recurrent Neural Networks, Proc of the 11th ACM Conference on Recommender Systems, pp. 130-137, (2017)
  • [10] RUOCCO M, SKREDE O S L, LANGSETH H., Inter-Session Modeling for Session-Based Recommendation, Proc of the 2nd Workshop on Deep Learning for Recommender Systems, pp. 24-31, (2017)
123