Skip to main content
SpringerLink
Log in

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

  • Published:
Applied Intelligence Aims and scope Submit manuscript

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.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  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 283–292

  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 91–100

  3. Baltrunas L, Makcinskas T, Ricci F (2010) Group recommendations with rank aggregation and collaborative filtering. In: ACM conference on recommender systems, pp 119–126

  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 2–8

  5. Berkovsky S, Freyne J (2010) Group-based recipe recommendations:analysis of data aggregation strategies. In: ACM conference on recommender systems, pp 111–118

  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 2082–2090

  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 239–246

  8. Crossen A, Budzik J, Hammond KJ (2002) Flytrap: intelligent group music recommendation. In: International conference on intelligent user interfaces, pp 184–185

  9. Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS) 22(1):143–177

    Article  Google Scholar 

  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 1895–1904

  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 355–369

  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 97–106

  13. Gori M, Pucci A, Roma V, Siena I (2007) Itemrank: a random-walk based scoring algorithm for recommender engines. IJCAI 7:2766–2771

    Google Scholar 

  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 9–16

  15. Hong M, Jung JJ, Piccialli F, Chianese A (2017) Social recommendation service for cultural heritage. Pers Ubiquit Comput 21:191–201

    Article  Google Scholar 

  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 847–855

  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 68:66–81

    Article  Google Scholar 

  18. Hou S, Zhou S, Liu W, Zheng Y (2018) Classifying advertising video by topicalizing high-level semantic concepts. Multimed Tools Appl 99:1–37

    Google Scholar 

  19. Jameson A, Smyth B (2007) Recommendation to groups. In: Springer

  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 337–344

  21. Kompan M, Bielikova M (2014) Group recommendations survey and perspectives. Comput Inf 33:1–31

    Google Scholar 

  22. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

    Article  Google Scholar 

  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 975–984

  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 674–683

  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 1291358

  26. Masthoff J (2011) Group recommender systems: combining individual models. In: Recommender systems handbook, pp 677–702

  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 1933–1942

  28. Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: bringing order to the web. Stanford InfoLab

  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 653–658

  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 146–146

  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 145–151

  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 2–4

  33. Salakhutdinov R, Mnih A (2007) Probabilistic matrix factorization. NIPS 1:2–1

    Google Scholar 

  34. Salehi-Abari A, Boutilier C (2015) Preference-oriented social networks: group recommendation and inference. In: ACM conference on recommender systems, pp 35–42

  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 1865–1874

  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 404–413

  37. Tong H, Faloutsos C, Pan J (2006) Fast random walk with restart and its applications. In: International conference on data mining, pp 613–622

  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 117–126

  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 28–33

  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 2018:1–13

    Google Scholar 

  41. Xu Y, Yi X, Zhang C (2006) A random walks method for text classification. In: SDM, SIAM. pp 340–347

  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 63–82

  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 163–172

  44. Zapata A, Prieto ME (2015) Evaluation and selection of group recommendation strategies for collaborative searching of learning objects. Int J Hum Comput Stud 76:22–39

    Article  Google Scholar 

  45. Zhang H, Cao L, Gao S (2014) A locality correlation preserving support vector machine. Pattern Recogn 47:3168–3178

    Article  MATH  Google Scholar 

  46. Zhang H, Lu J (2010) Creating ensembles of classifiers via fuzzy clustering and deflection. Fuzzy Sets Syst 161:1790–1802

    Article  MathSciNet  Google Scholar 

  47. Zhang S, Yao L, Sun A (2017) Deep learning based recommender system: a survey and new perspectives. In: ACM computing surveys

  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 635–644

Download references

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).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan Shi, China

    Shanshan Feng & Huaxiang Zhang

  2. School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Xuhui Qu, China

    Jian Cao & Yan Yao

Authors
  1. Shanshan Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huaxiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yan Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shanshan Feng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feng, S., Zhang, H., Cao, J. et al. Merging user social network into the random walk model for better group recommendation. Appl Intell 49, 2046–2058 (2019). https://doi.org/10.1007/s10489-018-1375-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-018-1375-z

Keywords

Search

Navigation