Advertisement

Journal of Computer Science and Technology

, Volume 33, Issue 4, pp 682–696 | Cite as

Exploiting Pre-Trained Network Embeddings for Recommendations in Social Networks

  • Lei Guo
  • Yu-Fei Wen
  • Xin-Hua Wang
Regular Paper
  • 73 Downloads

Abstract

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.

Keywords

social recommendation network embedding matrix factorization item sequential pattern 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

11390_2018_1849_MOESM1_ESM.pdf (718 kb)
ESM 1 (PDF 717 kb)

References

  1. [1]
    Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer, 2009, 42(8): 30-37.CrossRefGoogle Scholar
  2. [2]
    Salakhutdinov R, Mnih A. Probabilistic matrix factorization. In Proc. the 20th International Conference on Neural Information Processing Systems, December 2007, pp.1257-1264.Google Scholar
  3. [3]
    Ma H, Yang H, Lyu M R, King I. SoRec: Social recommendation using probabilistic matrix factorization. In Proc. the 17th ACM Conference on Information and Knowledge Management, October 2008, pp.931-940.Google Scholar
  4. [4]
    Ma H, King I, Lyu M R. Learning to recommend with social trust ensemble. In Proc. the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2009, pp.203-210.Google Scholar
  5. [5]
    Ma H, Zhou D, Liu C, Lyu M R, King I. Recommender systems with social regularization. In Proc. the 4th ACM International Conference on Web Search and Data Mining, February 2011, pp.287-296.Google Scholar
  6. [6]
    Guo L, Ma J, Jiang H R, Chen Z M, Xing C M. Social trust aware item recommendation for implicit feedback. Journal of Computer Science and Technology, 2015, 30(5): 1039-1053.MathSciNetCrossRefGoogle Scholar
  7. [7]
    Wang W, Yin H, Sadiq S, Chen L, Xie M, Zhou X. SPORE: A sequential personalized spatial item recommender system. In Proc. the 32nd IEEE International Conference on Data Engineering, May 2016, pp.954-965.Google Scholar
  8. [8]
    Chu W T, Tsai Y L. A hybrid recommendation system considering visual information for predicting favorite restaurants. World Wide Web, 2017, 20(6): 1313-1331.CrossRefGoogle Scholar
  9. [9]
    Ling D, Zhan M, Ellis D P W. Content-aware collaborative music recommendation using pre-trained neural networks. In Proc. the International Symposium/Conference on Music Information Retrieval, Oct. 2015, pp.295-301.Google Scholar
  10. [10]
    Zhao W X, Huang J, Wen J R. Learning distributed representations for recommender systems with a network embedding approach. In Proc. the Asia Information Retrieval Symposium, October 2016, pp.224-236.Google Scholar
  11. [11]
    Perozzi B, Alrfou R, Skiena S. DeepWalk: Online learning of social representations. In Proc. the Asia Information Retrieval Symposium, August 2014, pp.701-710.Google Scholar
  12. [12]
    Grover A, Leskovec J. Node2Vec: Scalable feature learning for networks. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, pp.855-864.Google Scholar
  13. [13]
    Goldberg D, Nichols D, Oki B M, Terry D. Using collaborative filtering to weave an information tapestry. Commun. ACM, 1992, 35(12): 61-70.CrossRefGoogle Scholar
  14. [14]
    Ma H, King I, Lyu M R. Effective missing data prediction for collaborative filtering. In Proc. the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2007, pp.39-46.Google Scholar
  15. [15]
    Li D, Lv Q, Shang L, Gu N. Item-based top-N recommendation resilient to aggregated information revelation. Knowledge-Based Systems, 2014, 67(3): 290-304.CrossRefGoogle Scholar
  16. [16]
    Canny J. Collaborative filtering with privacy via factor analysis. In Proc. the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 2002, pp.238-245.Google Scholar
  17. [17]
    Kamishima T, Akaho S, Asoh H, Sato I. Model-based approaches for independence-enhanced recommendation. In Proc. the 16th IEEE International Conference on Data Mining Workshops, December 2017, pp.860-867.Google Scholar
  18. [18]
    Yin H, Zhou X, Cui B, Wang H, Zheng K, Nguyen Q V H. Adapting to user interest drift for POI recommendation. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(15): 2566-2581.CrossRefGoogle Scholar
  19. [19]
    Hofmann T. Collaborative filtering via Gaussian probabilistic latent semantic analysis. In Proc. the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, July 2003, pp.259-266.Google Scholar
  20. [20]
    Menon A K, Elkan C. A log-linear model with latent features for dyadic prediction. In Proc. the IEEE International Conference on Data Mining, December 2010, pp.364-373.Google Scholar
  21. [21]
    Massa, P, Avesani P. Trust-aware recommender systems. In Proc. the ACM Conference on Recommender Systems, October 2007, pp.17-24.Google Scholar
  22. [22]
    Jamali M, Ester M. TrustWalker: A random walk model for combining trust-based and item-based recommendation. In Proc. the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, June 2009, pp.397-406.Google Scholar
  23. [23]
    Cao G, Kuang L. Identifying core users based on trust relationships and interest similarity in recommender system. In Proc. the 2016 IEEE International Conference on Web Services, June 2016, pp.284-291.Google Scholar
  24. [24]
    Li W, Ye Z, Xin M, Jin Q. Social recommendation based on trust and influence in SNS environments. Multimedia Tools and Applications, 2017, 76(9): 11585-11602.CrossRefGoogle Scholar
  25. [25]
    Yin H, Hu Z, Zhou X, Wang H, Zheng K, Nguyen Q V H, Sadiq S. Discovering interpretable geo-social communities for user behavior prediction. In Proc. IEEE International Conference on Data Engineering, May 2016, pp.942-953.Google Scholar
  26. [26]
    Ma H, King I, Lyu M R. Learning to recommend with explicit and implicit social relations. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 1-19.Google Scholar
  27. [27]
    Kannan R, Ishteva M, Park H. Bounded matrix factorization for recommender system. Knowledge and Information Systems, 2014, 39(3): 491-511.CrossRefGoogle Scholar
  28. [28]
    Yu H, Hsieh C, Si S, Dhillon I S. Parallel matrix factorization for recommender systems. Knowledge and Information Systems, 2014, 41(3): 793-819.CrossRefGoogle Scholar
  29. [29]
    Zhao Z L, Wang C D, Wan Y Y, Lai J H, Huang D. FTMF: Recommendation in social network with feature transfer and probabilistic matrix factorization. In Proc. the International Joint Conference on Neural Networks, July 2016, pp.847-854.Google Scholar
  30. [30]
    Liu Y, Wei W, Sun A, Miao C. Exploiting geographical neighborhood characteristics for location recommendation. In Proc. the 23rd ACM International Conference on Information and Knowledge Management, November 2014, pp.739-748.Google Scholar
  31. [31]
    Ma H. An experimental study on implicit social recommendation. In Proc. the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2013, pp.73-82.Google Scholar
  32. [32]
    Jamali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks. In Proc. the 4th ACM Conference on Recommender Systems, September 2010, pp.135-142.Google Scholar
  33. [33]
    Ma H, Zhou D, Liu C, Lyu M R, King I. Recommender systems with social regularization. In Proc. the 4th ACM International Conference on Web Search and Data Mining, February 2011, pp.287-296.Google Scholar
  34. [34]
    Tang J, Hu X, Gao H, Liu H. Exploiting local and global social context for recommendation. In Proc. the 23rd International Joint Conference on Artificial Intelligence, August 2013, pp.2712-2718.Google Scholar
  35. [35]
    Koren Y. Collaborative filtering with temporal dynamics. Communications of the ACM, 2010, 53(4): 89-97.CrossRefGoogle Scholar
  36. [36]
    Xiong L, Chen X, Huang T K, Schneider J G, Carbonell J G. Temporal collaborative filtering with Bayesian probabilistic tensor factorization. In Proc. the SIAM International Conference on Data Mining, April 2010, pp.211-222.Google Scholar
  37. [37]
    Liu N N, He L, Zhao M. Social temporal collaborative ranking for context aware movie recommendation. ACM Transactions on Intelligent Systems and Technology, 2013, 4(1): 1-26.Google Scholar
  38. [38]
    Lian D, Zhang Z, Ge Y, Zhang F, Yuan N J, Xie X. Regularized content-aware tensor factorization meets temporal-aware location recommendation. In Proc. IEEE International Conference on Data Mining, December 2016, pp.1029-1034.Google Scholar
  39. [39]
    Yin H, Cui B. Spatio-Temporal Recommendation in Social Media. Springer Singapore, 2016.Google Scholar
  40. [40]
    Zhang J D, Chow C Y. TICRec: A probabilistic framework to utilize temporal influence correlations for time-aware location recommendations. IEEE Transactions on Services Computing, 2016, 9(4): 633-646.CrossRefGoogle Scholar
  41. [41]
    Zhang J D, Chow C Y, Li Y. LORE: Exploiting sequential influence for location recommendations. In Proc. the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2014, pp.103-112.Google Scholar
  42. [42]
    Yin H, Cui B, Zhou X, Wang W, Huang Z, Sadiq S. Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. ACM Transactions on Information Systems, 2016, 35(2): Atricle No. 11.Google Scholar
  43. [43]
    Yin H, Chen H, Sun X, Wang H, Wang Y, Nguyen Q V H. SPTF: A scalable probabilistic tensor factorization model for semantic-aware behavior prediction. In Proc. IEEE International Conference on Data Mining, November 2017, pp.585-594.Google Scholar
  44. [44]
    Tong Y, Chen L, Zhou Z, Jagadish H V, Shou L, Lv W. SLADE: A smart large-scale task decomposer in crowdsourcing. IEEE Transactions on Knowledge and Data Engineering, 2018. (to be appeared)Google Scholar
  45. [45]
    Tong Y, She J, Ding B, Wang L, Chen L. Online mobile micro-task allocation in spatial crowdsourcing. In Proc. IEEE International Conference on Data Engineering, May 2016, pp.49-60.Google Scholar
  46. [46]
    Angela C R, Marl V O, Gensel J, Martin H. Contextual user profile for adapting information in nomadic environments. In Proc. the International Conference on Web Information Systems Engineering, December 2007, pp.337-349.Google Scholar
  47. [47]
    Lian D, Zhao C, Xie X, Sun G, Chen E, Rui Y. GeoMF: Joint geographical modeling and matrix factorization for point-of-interest recommendation. In Proc. the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2014, pp.831-840.Google Scholar
  48. [48]
    Zheng Y, Burke R, Mobasher B. The role of emotions in context-aware recommendation. In Proc. the 3rd Workshop on Human Decision Making in Recommender Systems, October 2013, pp.21-28.Google Scholar
  49. [49]
    Lecun Y, Bengio Y, Hinton G. Deep learning. Proceedings of the VLDB Endowment, 2015, 521(7553): 436-444.Google Scholar
  50. [50]
    Nguyen T T, Lauw H W. Representation learning for homophilic preferences. In Proc. the 10th ACM Conference on Recommender Systems, September 2016, pp.317-324.Google Scholar
  51. [51]
    Deng S, Huang L, Xu G, Wu X, Wu Z. On deep learning for trust-aware recommendations in social networks. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(5): 1164-1177.CrossRefGoogle Scholar
  52. [52]
    Yin H, Wang W, Wang H, Chen L, Zhou X. Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(11): 2537-2551.CrossRefGoogle Scholar
  53. [53]
    Kataria S, Agarwal A. Distributed representations for content-based and personalized tag recommendation. In Proc. the IEEE International Conference on Data Mining Workshop, November 2015, pp.1388-1395.Google Scholar
  54. [54]
    Yang C, Sun M, Zhao W Xin, Liu Z, Chang E Y. A neural network approach to jointly modeling social networks and mobile trajectories. ACM Transactions on Information Systems, 2016, 35(4): 36:1-36:28.Google Scholar
  55. [55]
    Xie M, Yin H, Wang H, Xu F, Chen W, Wang S. Learning graph-based POI embedding for location-based recommendation. In Proc. the 25th ACM International Conference on Information and Knowledge Management, October 2016, pp.15-24.Google Scholar
  56. [56]
    Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. Distributed representations of words and phrases and their compositionality. In Proc. the 26th International Conference on Neural Information Processing Systems, October 2013, pp.3111-3119.Google Scholar
  57. [57]
    Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q. LINE: Large-scale information network embedding. In Proc. the 24th International Conference on World Wide Web, May 2015, pp.1067-1077.Google Scholar
  58. [58]
    Liang D, Altosaar J, Charlin L, Blei D M. Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence. In Proc. the 10th ACM Conference on Recommender Systems, September 2016, pp.59-66.Google Scholar
  59. [59]
    Zhao WX, Li S, He Y, Chang E Y, Wen J R, Li X. Connecting social media to e-commerce: Cold-start product recommendation using microblogging information. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(5): 1147-1159.Google Scholar
  60. [60]
    Rennie J D M, Srebro N. Fast maximum margin matrix factorization for collaborative prediction. In Proc. the 22nd International Conference on Machine Learning, August 2005, pp.713-719.Google Scholar
  61. [61]
    Menon A K, Elkan C. Link prediction via matrix factorization. In Proc. the 2011 European Conference on Machine Learning and Knowledge Discovery in Databases, September 2011, pp.437-452.Google Scholar
  62. [62]
    Guo L, Ma J, Chen Z, Jiang H. Learning to recommend with social relation ensemble. In Proc. the 21st ACM International Conference on Information and Knowledge Management, October 2012, pp.2599-2602.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Postdoctoral Research Station of Management Science and EngineeringShandong Normal UniversityJinanChina
  2. 2.School of Management Science and EngineeringShandong Normal UniversityJinanChina
  3. 3.School of Information Science and EngineeringShandong Normal UniversityJinanChina

Personalised recommendations