Skip to main content
SpringerLink
Log in

Improving group recommendations via detecting comprehensive correlative information

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Traditionally, recommender systems are applied to recommending items to individual users. However, there has been a proliferation of recommender systems that try to make recommendations to user groups. Although several approaches were proposed to generate group recommendations, they made recommendations simply through aggregating individual ratings or individual predicted results, rather than comprehensively investigating the inherent relationships between members and the group, which can be used to improve the performance of group recommender systems. For this reason, these approaches continue to suffer from data sparsity and do not work well for recommending items to user groups. Therefore, we proposed a new approach for group recommendations based on random walk with restart (RWR) method. The goal of the work in this paper is describing groups’ preferences better by comprehensively detecting the correlative information among users, groups, and items, in order to alleviate the data sparsity problem and improve the performance of group recommender systems. In the proposed approach, we represent the relationships among users, groups, and items as a tripartite graph. Based on the tripartite graph, RWR can predict the relevance degrees between groups and unrated items by comprehensively detecting their relationships. Using these relevance degrees, we can describe a group’s preferences better so as to achieve a more accurate recommendation. In particular, we devised two recommendation algorithms based on different recommendation strategies. Finally, we conducted experiments to evaluate our method and compare it with other state-of-the-art methods using the real-world CAMRa2011 data-set. The results show the advantage of our approach over comparative ones.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Bao Y, Fang H, Zhang J (2014) Topicmf: simultaneously exploiting ratings and reviews for recommendation. In: 28th AAAI conference on artificial intelligence

  2. Berkovsky S, Freyne J (2010) Group-based recipe recommendations: analysis of data aggregation strategies. In: Proceedings of the 4th ACM conference on recommender systems. ACM, pp 111–118

  3. Berkovsky S, Freyne J, Coombe M (2009) Aggregation trade offs in family based recommendations. In: AI 2009: advances in artificial intelligence. Springer, Berlin Heidelberg New York, pp 646–655

    Chapter  Google Scholar 

  4. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th conference on uncertainty in artificial intelligence. Morgan Kaufmann, San Mateo, CA, pp 43–52

    Google Scholar 

  5. Chen Y-L, Cheng L-C, Chuang C-N (2008) A group recommendation system with consideration of interactions among group members. Expert Syst Appl 34(3):2082–2090

    Article  Google Scholar 

  6. Christensen IA, Schiaffino S (2011) Entertainment recommender systems for group of users. Expert Syst Appl 38(11):14127–14135

    Google Scholar 

  7. Chuang S-C, Xu Y-Y, Fu HC, Huang H-C (2006) A multiple-instance neural networks based image content retrieval system. In: 1st international conference on innovative computing, information and control, 2006. ICICIC’06. IEEE, vol 2, pp 412–415

  8. 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

  9. Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst (TOIS) 22(1):143–177

    Article  Google Scholar 

  10. 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. IEEE Trans Knowl Data Eng 19(3):355–369

    Article  Google Scholar 

  11. Freyne J, Smyth B (2006) Cooperating search communities. In: Adaptive hypermedia and adaptive web-based systems. Springer, Berlin Heidelberg New York, pp 101–110

    Chapter  Google Scholar 

  12. Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70

    Article  Google Scholar 

  13. Gorla J, Lathia N, Robertson S, Wang J (2013) Probabilistic group recommendation via information matching. In: Proceedings of the 22nd international conference on World Wide Web. International World Wide Web Conferences Steering Committee, pp 495–504

  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. Hofmann T, Hartmann D (2005) Collaborative filtering with privacy via factor analysis. In: Proceedings of the 2005 ACM symposium on applied computing, pp 791–795

  16. Hu X, Meng X, Wang L (2011) Svd-based group recommendation approaches: an experimental study of moviepilot. In: Proceedings of the 2nd challenge on context-aware movie recommendation. ACM, pp 23–28

  17. 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

  18. Kim J, Lee D, Chung K-Y (2014) Item recommendation based on context-aware model for personalized u-healthcare service. Multimed Tools Appl 71(2):855–872

    Article  Google Scholar 

  19. Lai C-C, Huang H-C, Tsai C-C (2009) A digital watermarking scheme based on singular value decomposition and micro-genetic algorithm. International Journal of Innovative Computing, Information and Control 5(7):1867–1873

    Google Scholar 

  20. Mao Y, Guo B-L, Yan Y, Sun W Multiple structure based saliency detection and its application in image retrieval

  21. Marlin B, Zemel RS (2004) The multiple multiplicative factor model for collaborative filtering. In: Proceedings of the 21st international conference on machine learning. ACM, p 73

  22. Masthoff J (2004) Group modeling: selecting a sequence of television items to suit a group of viewers. User Model User-Adap Inter 14(1):37–85

    Article  Google Scholar 

  23. Masthoff J (2011) Group recommender systems: combining individual models. In: Recommender systems handbook. Springer, Berlin Heidelberg New York, pp 677–702

    Chapter  Google Scholar 

  24. Min S-H, Han I (2005) Recommender systems using support vector machines. In: Web engineering. Springer, Berlin Heidelberg New York, pp 387–393

    Chapter  Google Scholar 

  25. Oconnor M, Cosley D, Konstan JA, Riedl J (2002) Polylens: a recommender system for groups of users. In: ECSCW 2001. Springer, Berlin Heidelberg New York, pp 199–218

    Chapter  Google Scholar 

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

  27. Pan J-Y, Yang H-J, Faloutsos C, Duygulu P (2004) Automatic multimedia cross-modal correlation discovery. In: Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 653–658

  28. Pan J-Y, Yang H-J, Faloutsos C, Duygulu P (2004) Gcap: graph-based automatic image captioning. In: Conference on computer vision and pattern recognition workshop, 2004. CVPRW’04. IEEE, pp 146–146

  29. Pappas N, Popescu-Belis A (2014) Combining content with user preferences for non-fiction multimedia recommendation: a study on ted lectures. Multimedia Tools and Applications, pp 1–23

  30. Qiao Y, Zhao Y (2015) Rotation invariant texture classification using principal direction estimation. In: Genetic and evolutionary computing. Springer, Berlin Heidelberg New York, pp 247–256

    Google Scholar 

  31. Rennie JD, Srebro N (2005) Fast maximum margin matrix factorization for collaborative prediction. In: Proceedings of the 22nd international conference on machine learning. ACM, pp 713–719

  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. In: NIPS, vol 1, pp 2–1

  34. Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Advances in artificial intelligence, vol 2009, p 4

  35. 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

  36. 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

  37. 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

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

  39. Zhang W, Liu C, Wang Z, Li G, Huang Q, Gao W (2014) Web video thumbnail recommendation with content-aware analysis and query-sensitive matching. Multimed Tools Appl 73(1):547–571

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China

    Shanshan Feng & Jian Cao

Authors
  1. Shanshan Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian Cao
    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., Cao, J. Improving group recommendations via detecting comprehensive correlative information. Multimed Tools Appl 76, 1355–1377 (2017). https://doi.org/10.1007/s11042-015-3135-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-3135-y

Keywords

Search

Navigation