Scalable Recommendation Models Fusing Multi-Source Heterogeneous Data

doi:10.13190/j.jbupt.2020-229

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (3): 106-111.doi: 10.13190/j.jbupt.2020-229

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Scalable Recommendation Models Fusing Multi-Source Heterogeneous Data

JI Zhen-yan¹, WU Meng-dan^1,2, YANG Chun¹, LI Jun-dong¹

1. School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China;
2. Institute of Software, Chinese Academy of Sciences, Beijing 100190, China

Received:2020-11-12 Online:2021-06-28 Published:2021-06-23

Abstract

Abstract: Social relationship plays an important role in life, and users are often affected by their friends' preferences. It is easier for users to choose items that their friends have purchased. In order to solve the cold start problem of the recommended system, a recommendation system that integrates social relationships is studied, and Bayesian personalized ranking review score social model and scalable Bayesian personalized ranking review score social model are proposed. The proposed fusion recommendation models integrate multi-source heterogeneous data such as scores, reviews, and social relationships from the data source level, introduce social relationships into the recommendation system through the user friend trust model, use the paragraph vector-distributed memory model to process review, use the fully connected neural network to process rating, and use an improved Bayesian personalized ranking model to optimize the ranking results. Experiments are conducted on the Yelp public dataset. It is shown that the recommendation accuracy of the two proposed models are better than other recommendation models.

Key words: recommendation system, multi-source heterogeneous data, social relation, hybrid model, data fusion

CLC Number:

TP391

JI Zhen-yan, WU Meng-dan, YANG Chun, LI Jun-dong. Scalable Recommendation Models Fusing Multi-Source Heterogeneous Data[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(3): 106-111.

References

[1] Fabbri F, Bonchi F, Boratto L, et al. The effect of homophily on disparate visibility of minorities in people re-commender systems[C]//International AAAI Conference on Web and Social Media.[S.l.]:AAAI, 2020:165-175.
[2] Gao Jingyue, Wang Xiting, Wang Yasha, et al. Explainable recommendation through attentive multi-view learning[C]//AAAI Conference on Artificial Intelligence. Hawaii:AAAI, 2019:3622-3629.
[3] Ji Zhenyan, Pi Huaiyu, Wei Wei, et al. Recommendation based on review texts and social communities:a hybrid model[J]. IEEE Access, 2019, 7:40416-40427.
[4] Wang Chengwei, Zhou Tengfei, Chen Chen, et al. Camo:a collaborative ranking method for content based recommendation[C]//AAAI Conference on Artificial Intelligence. Hawaii:AAAI, 2019:5224-5231.
[5] 王永, 邓江洲. 基于KL散度的用户相似性协同过滤算法[J]. 北京邮电大学学报, 2017, 40(2):110-114. Wang Yong, Deng Jiangzhou. User similarity collaborative filtering algorithm based on KL divergence[J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40(2):110-114.
[6] Liu Hao, Li Ting, Hu Renjun, et al. Joint representation learning for multi-modal transportation recommendation[C]//AAAI Conference on Artificial Intelligence. Hawaii:AAAI, 2019:1036-1043.
[7] Zheng Lei, Noroozi V, Yu P S. Joint deep modeling of users and items using reviews for recommendation[C]//Tenth ACM International Conference on Web Search and Data Mining. Cambridge:ACM, 2017:425-434.
[8] Chen Wei, Niu Zhengdong, Zhao Xiangyu, et al. A hybrid recommendation algorithm adapted in e-learning environments[J]. World Wide Web, 2014, 17(2):271-284.
[9] Zhang Shuai, Yao Lina, Sun Aixin, et al. Deep learning based recommender system:a survey and new perspectives[J]. ACM Computing Surveys (CSUR), 2019, 52(1):1-38.
[10] Milgram, Stanley. The small world problem[J]. Psychology Today, 1967, 2(1):60-67.
[11] Elkahky A M, Song Y, He X. A multi-view deep learning approach for cross domain user modeling in recommendation systems[C]//24th International Conference on World Wide Web. Florence:WWW, 2015:278-288.
[12] Krohn-Grimberghe A, Drumond L, Freudenthaler C, et al. Multi-relational matrix factorization using Bayesian personalized ranking for social network data[C]//Fifth ACM International Conference on Web Search and Data Mining. Washington:WSDM, 2012:173-182.
[13] Le Q, Mikolov T. Distributed representations of sentences and documents[C]//International Conference on Machine Learning. Beijing:ICML, 2014:1188-1196.
[14] Rendle S, Freudenthaler C, Gantner Z, et al. BPR:Bayesian personalized ranking from implicit feedback[EB/OL]. 2012(2012-05-09)[2020-11-12]. https://arxiv.org/abs/1205.2618.
[15] Al-Mashari, Majed, Zairi M. Revisiting BPR:a holistic review of practice and development[J]. Business Process Management Journal, 2000, 6(1):10-42.
[16] He R, McAuley J. VBPR:visual Bayesian personalized ranking from implicit feedback[C]//AAAI Conference on Artificial Intelligence. Arizona:AAAI, 2016:144-150.

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