Recommendation Algorithm Based on Multi-dimensional Feature Representation Learning in Complex Networks

doi:10.12068/j.issn.1005-3026.2022.03.008

Abstract

Abstract: Network representation learning can effectively solve the problem of data sparsity in recommendation. In this paper， LINE and DeepWalk in network representation learning were improved， and a hybrid recommendation algorithm was proposed to be applied to movie recommendation scene. The new algorithm generates three low dimensional feature vectors by learning user preference feature， user aversion feature and similar user feature. Three low dimensional feature vectors are linearly combined to form a user representation vector， and cosine similarity is used as the similarity index to recommend the movies associated with similar users to target users. Experimental results show that， compared with the suboptimal algorithm， the accuracy and F1 index of the proposed algorithm are improved by 12% and 7% respectively on MovieLens dataset， and 16% and 18% respectively on MovieTweetings dataset. The recommendation algorithm based on multi-dimensional feature representation learning proposed in this paper has significant advantages in movie recommendation scenes.

Key words: network representation learning; recommendation algorithm; multi-dimensional feature learning(MFL); LINE; DeepWalk

CLC Number:

TP181

DING Lai-xu， LIU Hong-juan. Recommendation Algorithm Based on Multi-dimensional Feature Representation Learning in Complex Networks[J]. Journal of Northeastern University(Natural Science), 2022, 43(3): 359-367.

References

[1]Perozzi B，Al-Rfou R，Skiena S.DeepWalk:online learning of social representations ［C］//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:Association for Computing Machinery，2014:701-710.
[2]Tang J，Qu M，Wang M，et al.LINE:large-scale information network embedding［C］// Proceedings of the WWW 2015—Proceedings of the 24th International Conference on World Wide Web.Florence:International World Wide Web Conferences Steering Committee，2015:1067-1077.
[3]Zhao W X，Huang J，Wen J R.Learning distributed representations for recommender systems with a network embedding approach［C］//Asia Information Retrieval Symposium.Cham:Springer，2016:224-236.
[4]Barkan O，Koenigstein N.ITEM2VEC:neural item embedding for collaborative filtering［C］// Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing，MLSP.Salerno:Institute of Electrical and Electronics Engineers，2016:1-6.
[5]Li Y，Chen W，Yan H.Learning graph-based embedding for time-aware product recommendation ［C］// Proceedings of the International Conference on Information and Knowledge Management.Singapore:Association for Computing Machinery，2017:2163-2166.
[6]Xu Y，Zhu Y，Shen Y，et al.Learning shared vertex representation in heterogeneous graphs with convolutional networks for recommendation［C］// Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).Macao:International Joint Conferences on Artificial Intelligence，2019:4620-4626.
[7]Cheng H T，Koc L，Harmsen J，et al.Wide & deep learning for recommender systems［C］// Proceedings of the ACM International Conference Proceeding Series.Boston:Association for Computing Machinery，2016:7-10.
[8]Guo H，Tang R，Ye Y，et al.DeepFM:a factorization-machine based neural network for CTR prediction［C］// Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).Melbourne:International Joint Conferences on Artificial Intelligence，2017:1725-1731.
[9]Mikolov T，Sutskever I，Chen K，et al.Distributed representations of words and phrases and their compositionality［C］// Proceedings of the Advances in Neural Information Processing Systems.Lake Tahoe:Neural Information Processing Systems Foundation，2013:3111-3119.
[10]涂存超，杨成，刘知远，等.网络表示学习综述［J］.中国科学(信息科学)，2017，47(8):980-996.(Tu Cun-chao，Yang Cheng，Liu Zhi-yuan，et al.Network representation learning:an overview ［J］.Scientia Sinica (Informationis)，2017，47(8):980-996.)
[11]Grover A，Leskovec J.Node2vec:scalable feature learning for networks［C］//Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Francisco:Association for Computing Machinery，2016:855-864.
[12]Nguyen D，Malliaros F D.BiasedWalk:biased sampling for representation learning on graphs［C］//2018 IEEE International Conference on Big Data(Big Data).Seattle:Institute of Electrical and Electronics Engineers，2018:4045-4053.
[13]Ribeiro L F R，Saverese P H P，Figueiredo D R.Struc2vec:learning node representations from structural identity［C］//Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Halifax:Association for Computing Machinery，2017:385-394.
[14]Wang D，Cui P，Zhu W.Structural deep network embedding［C］//Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Francisco:Association for Computing Machinery，2016:1225-1234.
[15]Wang J，Huang P，Zhao H，et al.Billion-scale commodity embedding for E-commerce recommendation in Alibaba［C］//Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.London:Association for Computing Machinery，2018:839-848.
[16]Mikolov T，Chen K，Corrado G，et al.Efficient estimation of word representations in vector space［C］//Proceeding of the 1st International Conference on Learning Representations.Scottsdale:International Conference on Learning Representation，2013:1-12.
[17]Harper F M，Konstan J A.The movielens datasets:history and context ［J］.ACM Transactions on Interactive Intelligent Systems，2015，5(4):1-19.
[18]Dooms S，Bellogin A，Pessemier T D，et al.A framework for dataset benchmarking and its application to a new movie rating dataset［J］.ACM Transactions on Intelligent Systems & Technology，2016，7(3):1-28.
[19]Donnat C，Zitnik M，Hallac D，et al.Learning structural node embeddings via diffusion wavelets［C］//Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.London:Association for Computing Machinery，2018:1320-1329.