Advertisement

Merging user social network into the random walk model for better group recommendation

  • Shanshan FengEmail author
  • Huaxiang Zhang
  • Jian Cao
  • Yan Yao
Article
  • 71 Downloads

Abstract

At present, most recommendation approaches used to suggest appreciate items for individual users. However, due to the social nature of human beings, group activities have become an integral part of our daily life, thus the popularity of group recommender systems has increased in the last years. Unfortunately, most existing approaches used in group recommender systems make recommendations through aggregating individual preferences or individual predictive results rather than comprehensively investigating users social features that govern their choices made within a group. Therefore, we propose a new group recommendation approach, it incorporates user social network into the random walk with restart model and variously detects the inherent associations among group members, which can help us to better describe groups preference and improve the performance of group recommender systems. Besides, on the basis of multifaceted associations incorporation, we apply a partitioned matrix computation method in the recommendation process to save computational and storage costs. The final experiment results on the real-world CAMRa2011 dataset demonstrates that the proposed approach can not only effectively predict groups’ preference, but also have faster performance and more stable than other baseline methods.

Keywords

Recommendation User social network Partitioned matrix computation 

Notes

Acknowledgments

The work is partially supported by the National Natural Science Foundation of China (Nos. 61572298, 61772322, 61702310, 61603161) and the Key Research and Development Foundation of Shandong Province (No. 2017GGX10117).

References

  1. 1.
    Agarwal D, Agarwal D, Agarwal D, Agarwal D (2016) An empirical study on recommendation with multiple types of feedback. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 283–292Google Scholar
  2. 2.
    Agarwal D (2010) B.-c Chen. flda: matrix factorization through latent dirichlet allocation. In: Proceedings of the third ACM international conference on web search and data mining, ACM, pp 91–100Google Scholar
  3. 3.
    Baltrunas L, Makcinskas T, Ricci F (2010) Group recommendations with rank aggregation and collaborative filtering. In: ACM conference on recommender systems, pp 119–126Google Scholar
  4. 4.
    Bao Y, Fang H, Zhang J (2014) Topicmf: simultaneously exploiting ratings and reviews for recommendation. In: Twenty-Eighth AAAI conference on artificial intelligence, pp 2–8Google Scholar
  5. 5.
    Berkovsky S, Freyne J (2010) Group-based recipe recommendations:analysis of data aggregation strategies. In: ACM conference on recommender systems, pp 111–118Google Scholar
  6. 6.
    Chen Y-L, Cheng L-C, Chuang C-N (2008) A group recommendation system with consideration of interactions among group members. In: Expert systems with applications, vol 34. Elsevier, pp 2082–2090Google Scholar
  7. 7.
    Craswell N, Szummer M (2007) Random walks on the click graph. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 239–246Google Scholar
  8. 8.
    Crossen A, Budzik J, Hammond KJ (2002) Flytrap: intelligent group music recommendation. In: International conference on intelligent user interfaces, pp 184–185Google Scholar
  9. 9.
    Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS) 22(1):143–177CrossRefGoogle Scholar
  10. 10.
    Ester M, Ester M, Liao Y, Bu J, Guan Z, Guan Z, Cai D (2016) The million domain challenge: broadcast email prioritization by cross-domain recommendation. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 1895–1904Google Scholar
  11. 11.
    Fouss F, Pirotte A, Renders J-M , Saerens M (2007) Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. In: IEEE transactions on knowledge and data engineering, vol 19. IEEE, pp 355–369Google Scholar
  12. 12.
    Gartrell M, Xing X, Lv Q, Beach A, Han R, Mishra S, Seada K (2010) Enhancing group recommendation by incorporating social relationship interactions. In: International ACM siggroup conference on supporting group work, group 2010. Sanibel Island, Florida, USA, November, pp 97–106Google Scholar
  13. 13.
    Gori M, Pucci A, Roma V, Siena I (2007) Itemrank: a random-walk based scoring algorithm for recommender engines. IJCAI 7:2766–2771Google Scholar
  14. 14.
    He J, Li M, Zhang H-J, Tong H, Zhang C (2004) Manifold-ranking based image retrieval. In: Proceedings of the 12th annual ACM international conference on multimedia. ACM, pp 9–16Google Scholar
  15. 15.
    Hong M, Jung JJ, Piccialli F, Chianese A (2017) Social recommendation service for cultural heritage. Pers Ubiquit Comput 21:191–201CrossRefGoogle Scholar
  16. 16.
    Hosseini SA, Alizadeh K, Khodadadi A, Arabzadeh A, Farajtabar M, Zha H, Rabiee HR (2017) Recurrent poisson factorization for temporal recommendation. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 847–855Google Scholar
  17. 17.
    Hou S, Chen L, Tao D, Zhou S, Liu W, Zheng Y (2017) Multi-layer multi-view topic model for classifying advertising video. Pattern Recogn 68:66–81CrossRefGoogle Scholar
  18. 18.
    Hou S, Zhou S, Liu W, Zheng Y (2018) Classifying advertising video by topicalizing high-level semantic concepts. Multimed Tools Appl 99:1–37Google Scholar
  19. 19.
    Jameson A, Smyth B (2007) Recommendation to groups. In: SpringerGoogle Scholar
  20. 20.
    Jin R, Chai JY, Si L (2004) An automatic weighting scheme for collaborative filtering. In: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 337–344Google Scholar
  21. 21.
    Kompan M, Bielikova M (2014) Group recommendations survey and perspectives. Comput Inf 33:1–31zbMATHGoogle Scholar
  22. 22.
    Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37CrossRefGoogle Scholar
  23. 23.
    Li H, Ge Y, Zhu H, Zhu H (2016) Point-of-interest recommendations Learning potential check-ins from friends. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 975–984Google Scholar
  24. 24.
    Liu X, Tian Y, Ye M, Lee WC (2012) Exploring personal impact for group recommendation. In: ACM international conference on information and knowledge management, pp 674–683Google Scholar
  25. 25.
    Logesh R, Subramaniyaswamy V (2016) A collaborative location based travel recommendation system through enhanced rating prediction for the group of users. In: Computational intelligence and neuroscience, vol 2016, p 1291358Google Scholar
  26. 26.
    Masthoff J (2011) Group recommender systems: combining individual models. In: Recommender systems handbook, pp 677–702Google Scholar
  27. 27.
    Okura S, Tagami Y, Ono S, Tajima A (2017) Embedding-based news recommendation for millions of users. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 1933–1942Google Scholar
  28. 28.
    Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: bringing order to the web. Stanford InfoLabGoogle Scholar
  29. 29.
    Pan J-Y, Yang H-J, Faloutsos C, Duygulu P (2004) Automatic multimedia cross-modal correlation discovery. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 653–658Google Scholar
  30. 30.
    Pan J-Y, Yang H-J, Faloutsos C, Duygulu P (2004) Gcap: Graph-based automatic image captioning. In: 2004. CVPRW’04. Conference on computer vision and pattern recognition workshop. IEEE, pp 146–146Google Scholar
  31. 31.
    Qiao Z, Zhang P, Cao Y, Zhou C, Guo L, Fang B (2014) Combining heterogenous social and geographical information for event recommendation. In: Twenty-Eighth AAAI conference on artificial intelligence, pp 145–151Google Scholar
  32. 32.
    Said A, Berkovsky S, De Luca EW (2011) Group recommendation in context. In: Proceedings of the 2nd challenge on context-aware movie recommendation. ACM, pp 2–4Google Scholar
  33. 33.
    Salakhutdinov R, Mnih A (2007) Probabilistic matrix factorization. NIPS 1:2–1Google Scholar
  34. 34.
    Salehi-Abari A, Boutilier C (2015) Preference-oriented social networks: group recommendation and inference. In: ACM conference on recommender systems, pp 35–42Google Scholar
  35. 35.
    Sun L, Liu C, Guo C, Xie Y, Xie Y (2016) Data-driven automatic treatment regimen development and recommendation. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 1865–1874Google Scholar
  36. 36.
    Tong H, Faloutsos C (2006) Center-piece subgraphs: problem definition and fast solutions. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 404–413Google Scholar
  37. 37.
    Tong H, Faloutsos C, Pan J (2006) Fast random walk with restart and its applications. In: International conference on data mining, pp 613–622Google Scholar
  38. 38.
    Urban J, Jose JM (2006) Adaptive image retrieval using a graph model for semantic feature integration. In: Proceedings of the 8th ACM international workshop on multimedia information retrieval. ACM, pp 117–126Google Scholar
  39. 39.
    Wang L, Meng X, Zhang Y, Shi Y (2010) New approaches to mood-based hybrid collaborative filtering. In: Proceedings of the workshop on context-aware movie recommendation. ACM, pp 28–33Google Scholar
  40. 40.
    Wu H, Zhang H, Cui L, Wang X (2018) A heuristic model for supporting users decision-making in privacy disclosure for recommendation. Sec Commun Netw 2018:1–13Google Scholar
  41. 41.
    Xu Y, Yi X, Zhang C (2006) A random walks method for text classification. In: SDM, SIAM. pp 340–347Google Scholar
  42. 42.
    Yu Z, Zhou X, Hao Y, Gu J (2006) Tv program recommendation for multiple viewers based on user profile merging. In: User modeling and user-adapted interaction, vol 16. Springer, pp 63–82Google Scholar
  43. 43.
    Yuan Q, Cong G, Lin CY (2014) Com: a generative model for group recommendation. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 163–172Google Scholar
  44. 44.
    Zapata A, Prieto ME (2015) Evaluation and selection of group recommendation strategies for collaborative searching of learning objects. Int J Hum Comput Stud 76:22–39CrossRefGoogle Scholar
  45. 45.
    Zhang H, Cao L, Gao S (2014) A locality correlation preserving support vector machine. Pattern Recogn 47:3168–3178CrossRefGoogle Scholar
  46. 46.
    Zhang H, Lu J (2010) Creating ensembles of classifiers via fuzzy clustering and deflection. Fuzzy Sets Syst 161:1790–1802MathSciNetCrossRefGoogle Scholar
  47. 47.
    Zhang S, Yao L, Sun A (2017) Deep learning based recommender system: a survey and new perspectives. In: ACM computing surveysGoogle Scholar
  48. 48.
    Zhao H, Yao Q, Li J, Song Y, Lee DL (2017) Meta-graph based recommendation fusion over heterogeneous information networks. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 635–644Google Scholar

Copyright information

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

Authors and Affiliations

  • Shanshan Feng
    • 1
    Email author
  • Huaxiang Zhang
    • 1
  • Jian Cao
    • 2
  • Yan Yao
    • 2
  1. 1.School of Information Science and EngineeringShandong Normal UniversityJinan ShiChina
  2. 2.School of Electronic Information and Electrical EngineeringShanghai Jiaotong UniversityXuhui QuChina

Personalised recommendations