›› 2018, Vol. 33 ›› Issue (4): 682-696.doi: 10.1007/s11390-018-1849-9

Special Issue: 3; Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Recommender Systems with Big Data • Previous Articles     Next Articles

Exploiting Pre-Trained Network Embeddings for Recommendations in Social Networks

Lei Guo1,2, Member, CCF, Yu-Fei Wen2, Xin-Hua Wang3   

  1. 1 Postdoctoral Research Station of Management Science and Engineering, Shandong Normal University Jinan 250014, China;
    2 School of Management Science and Engineering, Shandong Normal University, Jinan 250014, China;
    3 School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China
  • Received:2018-01-15 Revised:2018-05-18 Online:2018-07-05 Published:2018-07-05
  • About author:Lei Guo received his Ph.D. degree in computer architecture from Shandong University, Jinan, in 2015. Now he is a lecturer and master supervisor in the School of Management Science and Engineering, Shandong Normal University, Jinan. His research interests include information retrieval, social network, and recommender system.
  • Supported by:

    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61602282, 61772321, 61472231 and 71301086, and the China Postdoctoral Science Foundation under Grant No. 2016M602181.

Recommender systems as one of the most efficient information filtering techniques have been widely studied in recent years. However, traditional recommender systems only utilize user-item rating matrix for recommendations, and the social connections and item sequential patterns are ignored. But in our real life, we always turn to our friends for recommendations, and often select the items that have similar sequential patterns. In order to overcome these challenges, many studies have taken social connections and sequential information into account to enhance recommender systems. Although these existing studies have achieved good results, most of them regard social influence and sequential information as regularization terms, and the deep structure hidden in social networks and rating patterns has not been fully explored. On the other hand, neural network based embedding methods have shown their power in many recommendation tasks with their ability to extract high-level representations from raw data. Motivated by the above observations, we take the advantage of network embedding techniques and propose an embedding-based recommendation method, which is composed of the embedding model and the collaborative filtering model. Specifically, to exploit the deep structure hidden in social networks and rating patterns, a neural network based embedding model is first pre-trained, where the external user and item representations are extracted. Then, we incorporate these extracted factors into a collaborative filtering model by fusing them with latent factors linearly, where our method not only can leverage the external information to enhance recommendation, but also can exploit the advantage of collaborative filtering techniques. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed method and the importance of these external extracted factors.

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