Springer Nature is making Coronavirus research free. View research | View latest news | Sign up for updates

Integrating a weighted-average method into the random walk framework to generate individual friend recommendations

  • 78 Accesses

  • 6 Citations

Abstract

Friend recommendation is a fundamental service in both social networks and practical applications, and is influenced by user behaviors such as interactions, interests, and activities. In this study, we first conduct in-depth investigations on factors that affect recommendation results. Next, we design Friend++, a hybrid multi-individual recommendation model that integrates a weighted average method (WAM) into the random walk (RW) framework by seamlessly employing social ties, behavior context, and personal information. In Friend++, the first plus signifies recommending a new friend through network features, while the second plus stands for using node features. To verify our method, we conduct experiments on three social datasets crawled from the Sina microblog system (Weibo). Experimental results show that the proposed method significantly outperforms six baseline methods in terms of recall, precision, F1-measure, and MAP. As a final step, we describe a case study that demonstrates the scalability and universality of our method. Through discussion, we reach a meaningful conclusion: although common interests are more important than user activities in making recommendations, user interactions may be the most important factor in finding the most appropriate potential friends.

This is a preview of subscription content, log in to check access.

References

  1. 1

    Doina A D, Simone S. Using context to get novel recommendation in internet message streams. In: Proceedings of the 24th International Conference on World Wide Web. New York: ACM, 2015. 783–786

  2. 2

    Cai Y, Leung H, Li Q, et al. Typicality-based collaborative filtering recommendation. IEEE Trans Knowl Data Eng, 2014, 26: 766–779

  3. 3

    Erheng Z, Nathan L, Yue S, et al. Building discriminative user profiles for large-scale content recommendation. In: Proceedings of the the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2015. 2277–2286

  4. 4

    Yang Z, Tang J, Zhang J, et al. Topic-level random walk through probabilistic model. In: Proceedings of Asia-Pacific Web Conference and Web-Age Information Management. Berlin: Springer-Verlag, 2009. 162–173

  5. 5

    Wang C, Tang J, Sun J, et al. Dynamic social influence analysis through time-dependent factor graphs. In: Proceedings of the 2011 International Conference on Social Networks Analysis and Mining. New York: ACM, 2011. 239–246

  6. 6

    Gong J B, Gao X X, Song Y Q, et al. Individual friends recomme dation based on random walk with restart in social networks. In: Proceedings of Chinese National Conference on Social Media Processing. Berlin: Springer-Verlag, 2016. 123–133

  7. 7

    Oh H, Kim J, Kim S, et al. A probability-based trust prediction model using trust-message passing. In: Proceedings of the 22nd International Conference on World Wide Web. New York: ACM, 2013. 161–162

  8. 8

    Wang P, Xu B W, Wu Y R, et al. Link prediction in social networks: the state-of-the-art. Sci China Inf Sci, 2017, 58: 011101

  9. 9

    Paul S, Boutsidis C, Magdon-Ismail M, et al. Random projections for linear support vector machines. ACM Trans Knowl Discov Data, 2014, 8: 1–25

  10. 10

    Chen W L, Chen Y X, Mao Y, et al. Density-based logistic regression. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2013. 140–148

  11. 11

    Gong J B, Wang L, Sun S T, et al. iBole: a hybrid multi-layer architecture for doctor recommendation in medical social networks. J Comput Sci Technol, 2015, 30: 1073–1081

  12. 12

    Tran V, Michael G. A spatial LDA model for discovering regional communities. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. New York: ACM, 2013. 162–168

  13. 13

    Hakan B, Pinar K. Context-aware friend recommendation for location based social networks using random walk. In: Proceedings of the 25th International Conference on World Wide Web. New York: ACM, 2016. 531–536

  14. 14

    Yao L, Wang L N, Pan L, et al. Link prediction based on common-neighbors for dynamic social network. Procedia Comput Sci, 2016, 83: 82–89

  15. 15

    Hakan B, Pinar K. Context-aware location recommendation by using a random walk-based approach. Knowl Inf Syst, 2016, 47: 241–260

  16. 16

    Page L, Brin S, Motwani R, et al. The Pagerank Citation Ranking: Bringing Order to the Web. Technical Report 66. 1999

  17. 17

    Li R-H, Yu J X, Huang X, et al. Random-walk domination in large graphs. In: Proceedings of IEEE 30th International Conference on Data Engineering, Chicago, 2014. 736–747

  18. 18

    Chen H H, Jin H, Cui X L. Hybrid followee recommendation in microblogging systems. Sci China Inf Sci, 2017, 60: 012102

  19. 19

    Tang J, Zhang J. A discriminative approach to topic-based citation recommendation. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining. Berlin: Springer-Verlag, 2009. 572–579

  20. 20

    Zhou T, Lü L Y, Zhang Y-C. Predicting missing links via local information. Eur Phys J, 2009, 71: 623–630

  21. 21

    Zhang C Y, Liang H W, Wang K. Trip recommendation meets real-world constraints: POI availability, diversity, and traveling time uncertainty. ACM Trans Inf Syst, 2016, 35: 1–28

  22. 22

    Sheng Y L, Jin R M. Learning personal social latent factor model for social recommendation. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2012. 1303–1311

  23. 23

    Bisio F, Meda C, Zunino R, et al. Real-time monitoring of Twitter traffic by using semantic networks. In: Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. New York: ACM, 2013. 966–969

  24. 24

    Yang X W, Harald S, Liu Y. Circle-based recommendation in online social networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2012. 1267–1275

  25. 25

    Liu Q, Li Z G, Lui J C, et al. PowerWalk: scalable personalized pagerank via random walks with vertex-centric decomposition. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. New York: ACM, 2016. 195–204

  26. 26

    Cheng H, Zhou Y, Yu J X. Clustering large attributed graphs: a balance between structural and attribute similarities. ACM Trans Knowl Discov Data, 2011, 5: 12

  27. 27

    Dong Y X, Tang J, Wu S, et al. Link prediction and recommendation across heterogeneous social networks. In: Proceedings of IEEE 12th International Conference on Data Mining. Washington: IEEE Computer Society, 2012. 181–190

  28. 28

    Wu S-H, Chien H-H, Lin K-H, et al. Learning the consistent behavior of common users for target node prediction across social networks. In: Proceedings of the 31st International Conference on Machine Learning, Beijing, 2014. 32: 298–306

  29. 29

    Benedikt L, Katja H, Jürgen Z. Blended recommending: integrating interactive information filtering and algorithmic recommender techniques. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. New York: ACM, 2015. 975–984

  30. 30

    Chen W, Hsu W, Mong L. Modeling user’s receptiveness over time for recommendation. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2013. 373–382

  31. 31

    Wang Z Y, Zhou Y, Tang J, et al. The prediction of venture capital co-investment based on structural balance theory. IEEE Trans Knowl Data Eng, 2016, 28: 1568–1569

  32. 32

    Tang J, Jin R M, Zhang J. A topic modeling approach and its integration into the random walk framework for academic search. In: Proceedings of IEEE International Conference on Data Mining. New York: ACM, 2008. 1055–1060

  33. 33

    Ye J H, Cheng H, Zhu Z, et al. Predicting positive and negative links in signed social networks by transfer learning. In: Proceedings of the 22nd International Conference on World Wide Web. New York: ACM, 2013. 1477–1488

  34. 34

    Huang J B, Huangfu X J, Sun H, et al. Backward path growth for efficient mobile sequential recommendation. IEEE Trans Knowl Data Eng, 2015, 27: 46–60

  35. 35

    Song D J, Meyer D A, Tao D C. Efficient latent link recommendation in signed networks. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2015. 1105–1114

Download references

Acknowledgements

This work was supported by National High Technology Research and Development Program of China (863 Program) (Grant No. 2015AA124102), Hebei Natural Science Foundation of China (Grant No. F2015203280), Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing (Grant No. 214125), National Natural Science Foundation of China (Grant No. 61303130), Graduate Innovation Funded Program of Yanshan University (Grant No. 2017XJSS028), and Innovation Zone Project Program for Science and Technology of China’s National Defense (Grand No. 2017-0001-863015-0009).

Author information

Correspondence to Xiaoxia Gao.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gong, J., Gao, X., Cheng, H. et al. Integrating a weighted-average method into the random walk framework to generate individual friend recommendations. Sci. China Inf. Sci. 60, 110104 (2017). https://doi.org/10.1007/s11432-017-9243-7

Download citation

Keywords

  • multi-individual friend recommendation architecture
  • behavior context analysis
  • Intimacy degree
  • random walk framework
  • social networks