| Computer Technology |
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| SeqRec: sequential-based recommendation model with long-term preference and instant interest |
Yan ZHANG( ),Bin GUO*(),Qian-ru WANG,Jing ZHANG,Zhi-wen YU |
| School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China |
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Abstract The user’s stable long-term preferences and dynamic instant interests were obtained by modeling on the user’s historical behavior records, and the user preferences were aggregated for personalized recommendation. Firstly, the users’ reviews on the items were extracted to represent the characteristics of the items. Secondly, users’ historical behavior records were used to represent their stable long-term preferences, and query data was used to model their instant interests. Third, the user’s final preferences were aggregated by assigning different weights to the long-term preferences and instant interests through the attention mechanism. Experiments on real data sets of Amazon were conducted to evaluate the performance of SeqRec model, and results show that it is superior to the current state-of-the-art sequential recommendation methods more than 10% in recall rate and percision ratio. Meanwhile, SeqRec model proves that the long-term preferences and instant interests of different users have different influences on their next purchases.
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Received: 02 January 2020
Published: 06 July 2020
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Corresponding Authors:
Bin GUO
E-mail: 1065120019@qq.com;guob@nwpu.edu.cn
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SeqRec：基于长期偏好和即时兴趣的序列推荐模型
通过对用户的历史购物序列进行建模，得到用户稳定的长期偏好和动态的即时兴趣，并聚合长期偏好和即时兴趣进行个性化推荐. 提取用户对商品的评论内容用于表示商品的特征；使用递归神经网络从用户的历史购买序列数据中学习用户稳定的长期偏好，使用提问数据对用户不断变化的即时兴趣进行建模；通过注意力机制为长期偏好以及即时兴趣分配不同的权重，得到用户最终偏好的向量表示. 在亚马逊真实数据集上的实验结果表明，SeqRec模型在召回率和精确率上比当前主流的序列推荐方法至少超出10%；SeqRec模型证明不同用户的长期偏好和即时兴趣对其下次购买物品的影响程度不同.
关键词：
序列数据,
个性化推荐,
长期偏好,
即时兴趣,
注意力机制
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| [1] |
KARATZOGLOU A, BALTRUNAS L, SHI Y. Learning to rank for recommender systems [C] // Proceedings of the 7th ACM Conference on Recommender Systems. Hong Kong: ACM, 2013: 493-494.
|
|
|
| [2] |
KOREN Y, BELL R, VOLINSKY C Matrix factorization techniques for recommender systems[J]. Computer, 2009, (8): 30- 37
|
|
|
| [3] |
KOREN Y. Collaborative filtering with temporal dynamics [C] // Proceedings of the 15th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining. Pairs: ACM, 2009: 447-456.
|
|
|
| [4] |
LIU Q, ZENG X Y, ZHU H S, et al. Mining indecisiveness in customer behaviors [C] // 2015 IEEE International Conference on Data Mining. Atlantic City: IEEE, 2015: 281-290.
|
|
|
| [5] |
ZHANG F Z, YUAN N J, Lian D F, et al. Collaborative knowledge base embedding for recommender systems [C] // Proceedings of the 22nd ACM SIGKDD international Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 2016: 353-362.
|
|
|
| [6] |
SHANG S, DING R G, ZHENG K, et al Personalized trajectory matching in spatial networks[J]. The VLDB Journal—The International Journal on Very Large Data Bases, 2014, 23 (3): 449- 468
doi: 10.1007/s00778-013-0331-0
|
|
|
| [7] |
YAP G E, LI X L, PHILIP S Y. Effective next-items recommendation via personalized sequential pattern mining [C] // International Conference on Database Systems for advanced applications. Berlin: Springer, 2012: 48-64.
|
|
|
| [8] |
GRBOVIC M, RADOSAVLJEVIC V, DJURIC N, et al. E-commerce in your inbox: product recommendations at scale [C] // Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Sydney: ACM, 2015: 1809-1818.
|
|
|
| [9] |
DONG D S, ZHENG X L, ZHANG R X, et al. Recurrent collaborative filtering for unifying general and sequential recommender [C] // International Joint Conferences on Artificial Intelligence. Stockholm: IJCAI, 2018: 3350–3356.
|
|
|
| [10] |
NIU S Z, ZHANG R Z. Collaborative sequence prediction for sequential recommender [C] // Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Singapore: ACM, 2017: 2239-2242.
|
|
|
| [11] |
YU F, LIU Q, WU S, et al. A dynamic recurrent model for next basket recommendation [C] // Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. Pisa: ACM, 2016: 729-732.
|
|
|
| [12] |
ZHANG Y Y, DAI H J, XU C, et al. Sequential click prediction for sponsored search with recurrent neural networks [C] // Twenty-Eighth AAAI Conference on Artificial Intelligence. Québec: AAAI, 2014.
|
|
|
| [13] |
RICCI F, ROKACH L, SHAPIRA B, et al. Introduction to recommender systems handbook [M]. Boston: Springer, 2011: 1-35.
|
|
|
| [14] |
MNIH A, SALAKHUTDINOV R R. Probabilistic matrix factorization [C] // In Advances in Neural Information Processing Systems. Vancouver: NIPS, 2008, 1257–1264.
|
|
|
| [15] |
BELL R M, KOREN Y. Scalable collaborative filtering with jointly derived neighborhood interpolation weights [C] // Seventh IEEE International Conference on Data Mining. Omaha: IEEE, 2007: 43-52.
|
|
|
| [16] |
WANG J, TANG Q. A probabilistic view of neighborhood-based recommendation methods [C] // 2016 IEEE 16th International Conference on Data Mining Workshops. Barcelona: IEEE, 2016: 14-20.
|
|
|
| [17] |
ZHAO H K, LIU Q, GE Y, et al. Group preference aggregation: a nash equilibrium approach [C] // 2016 IEEE 16th International Conference on Data Mining. Barcelona: IEEE, 2016: 679-688.
|
|
|
| [18] |
TANG J X, WANG K. Personalized top-n sequential recommendation via convolutional sequence embedding [C] // Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. Los Angeles: ACM, 2018: 565-573.
|
|
|
| [19] |
HE X N, LIAO L Z, ZHANG H W, et al. Neural collaborative filtering [C] // Proceedings of the 26th International Conference on World Wide Web. Switzerland: ACM, 2017: 173-182.
|
|
|
| [20] |
RENDLE S, FREUDENTHALER C, SCHMDITHIEME L. Factorizing personalized Markov chains for next-basket recommendation [C] // In Proceedings of the 19th international conference on World Wide Web. Raleigh: ACM, 811–820.
|
|
|
| [21] |
WANG P F, GUO J F, LAN Y Y, et al. Learning hierarchical representation model for next basket recommendation [C] // Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2015: 403-412.
|
|
|
| [22] |
HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-based recommendations with recurrent neural networks[J]. arXiv preprint, arXiv: 1511.06939, 2015.
|
|
|
| [23] |
LI J, REN P J, CHEN Z M, et al. Neural attentive session-based recommendation [C] // Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Singapore: ACM, 2017: 1419-1428.
|
|
|
| [24] |
DONKERS T, LOEPP B, ZIEGLER J. Sequential user-based recurrent neural network recommendations [C] // Proceedings of the Eleventh ACM Conference on Recommender Systems. Como: ACM, 2017: 152-160.
|
|
|
| [25] |
SCHUSTER M, PALIWAL K K Bidirectional recurrent neural networks[J]. IEEE Transactions on Signal Processing, 1997, 45 (11): 2673- 2681
doi: 10.1109/78.650093
|
|
|
| [26] |
HOCHREITER S, SCHMIDHUBER J Long short-term memory[J]. Neural Computation, 1997, 9 (8): 1735- 1780
|
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