计算机技术 |
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SeqRec:基于长期偏好和即时兴趣的序列推荐模型 |
张岩( ),郭斌*( ),王倩茹,张靖,於志文 |
西北工业大学 计算机学院,陕西 西安 710072 |
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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 |
引用本文:
张岩,郭斌,王倩茹,张靖,於志文. SeqRec:基于长期偏好和即时兴趣的序列推荐模型[J]. 浙江大学学报(工学版), 2020, 54(6): 1177-1184.
Yan ZHANG,Bin GUO,Qian-ru WANG,Jing ZHANG,Zhi-wen YU. SeqRec: sequential-based recommendation model with long-term preference and instant interest. Journal of ZheJiang University (Engineering Science), 2020, 54(6): 1177-1184.
链接本文:
http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.06.015
或
http://www.zjujournals.com/eng/CN/Y2020/V54/I6/1177
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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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