Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (1): 115-120.doi: 10.11896/jsjkx.201200192

• Database & Big Data & Data Science • Previous Articles     Next Articles

Convolutional Sequential Recommendation with Temporal Feature and User Preference

CHEN Jin-peng, HU Ha-lei, ZHANG Fan, CAO Yuan, SUN Peng-fei   

  1. School of Computer Science(National Pilot Software Engineering School),Beijing University of Posts and Telecommunications,Beijing 100876, China
  • Received:2020-12-22 Revised:2021-04-19 Online:2022-01-15 Published:2022-01-18
  • About author:CHEN Jin-peng,born in 1985,Ph.D,associate professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include social network analysis,recommendation system,data mining,and machine lear-ning.
  • Supported by:
    National Natural Science Foundation of China(61702043).

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Abstract: At present,recommendation system has been widely used in our life,which greatly facilitates people's life.The traditional recommendation method mainly analyzes the interaction between users and items and considers the history of users and items,and only obtains the user's preference for items in the past.The sequential recommendation system,by analyzing the interaction sequence of items in the recent period of time and considering the relevance between the user's previous and subsequent behaviors,can obtain user's preference for items in short term.It emphasizes the short-term connection between user and item,while ignoring the relationship between the attributes of the item.Aiming at the above problems,this paper presents a convolutional embedding recommendation with time and user preference (CERTU) model.This model can analyze the relations between items.It can obtain dynamic changes in user preferences.The model also considers the influence of individual item and multiple items to the next item.Experiments show that the performance of CERTU model is better than that of the current baseline method.

Key words: Convolutional neural network, Recommendation system, Sequential recommendation, Temporal feature, User preferences

CLC Number: 

  • TP311
[1]WANG C Y,ZHANG M,MA W Z,et al.Make It a Chorus:Knowledge -and Time-aware Item Modeling for Sequential Re-commendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.2020:109-118.
[2]STEFFEN R,CHRISTOPH F,LARS S.Factorizing persona-lized markov chains for next-basket recommendation[C]//Proceedings of the 19th International Conference on World Wide Web.2010:811-820.
[3]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 Reasearch and Development in Information Retrieval.2015:403-412.
[4]RUINING H,JULIAN M.Fusing Similarity Models with Mar-kov Chains for Sparse Sequential Recommendation[C]//2016 IEEE 16th International Conference on Data Mining (ICDM).2016:191-200.
[5]RUSLAN S,ANDRIY M,GEOFFREY H.Restricted Boltz-mann machines for collaborative filtering[C]//Proceedings of the 24th International Conference and Machine Learning.2007:791-798.
[6]LECUN Y,BOTTOU L,BENGIO Y.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324.
[7]ZHENG L,NOROOZI V,YU P S.Joint Deep Modeling of Users and ItemsUsing Reviews for Recommendation[C]//Proceedings of the Tenth ACM International Conference on Web Search and Data Mining.2017:425-434.
[8]TANG J X,WANG K.Personalized Top-N Sequential Recom-mendation via Convolutional Sequence Embedding[C]//Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining.2018:565-573.
[9]SUN K,QIAN T,CHEN T,et al.Where to Go Next:Modeling Long-and Short-Term User Preferences for Point-of-Interest Recommendation[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:214-221.
[10]GUO Q,SUN Z,ZHANG J,et al.An Attentional Recurrent Neural Network for Personalized Next Location Recommendation[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:83-90.
[11]LIU D,LIAN J,WANG S,et al.KRED:Knowledge-Aware Do-cument Representation for News Recommendations[C]//Fourteenth ACM Conference on Recommender Systems.2020:200-209.
[12]ZHAN W J,HONG Z L,FANG L P,et al.Collaborative Filtering Recommendation Algorithm Based on Adversarial Learning[J].Computer Science,2021,48(7):172-177.
[13]LIANG H H,GU T L,BIN C Z,et al.Combining User-end and Item-end Knowledge Graph Learning for Personalized Recommendation[J].Computer Science,2021,48(5):109-116.
[14]BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating embeddings for modeling multi-relational data[J].Advances in Neural Information Processing Systems,2013,26:2787-2795.
[15]STEFFEN R,CHRISTOPH F,ZENO G.BPR:Bayesian perso-nalized ranking from implicit feedback[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence.2009:452-461.
[16]ALEXANDROS K,XAVIER A,LINAS B.Multiverse recom-mendation:n-dimensional tensor factorization for context-aware collaborative filtering[C]//Proceedings of the 4th ACM Confe-rence on Recommender Systems.2010:79-86.
[17]BALÁZS H,ALEXANDROS K,LINAS B.Session-based re-commendations with recurrent neural networks[C]//International Conference on Learning Representations.2016:1-10.
[18]PABLO L,CHEN L,YU H.Modeling user session and intent with an attention-based encoder-decoder architecture[C]//Proceedings of the 11th ACM Conference on Recommender Systems.2017:147-151.
[19]ZHANG Y F,AI Q Y,CHEN X,et al.Learning over know-ledge-base embeddings for recommendation[C]//Special Interest Group on Information Retrieval.2018:8-12.
[20]WANG C Y,ZHANG M,MA W Z,et al.Modeling Item-Specific Temporal Dynamics of Repeat Consumption for Recommender Systems[C]//The World Wide Web Conference.2019:1977-1987.
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