Skip to main content
SpringerLink
Log in

Alleviating the cold-start problem by incorporating movies facebook pages

  • Published:
Cluster Computing Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Recommender systems are generally known as predictive ecosystem which recommends an appropriate list of items that may imply their similar preference or interest. Nevertheless, most discussed issues in recommendation system research domain are the cold-start problem. In this paper we proposed a novel approach to address this problem by combining similarity values obtain from a movie “Facebook Pages”. To achieve this, we first compute users’ similarity according to the rating cast on our Movie Rating System. Then, we combined similarity value obtain from user’s genre interest in “Like” information extracted from “Facebook Pages”. Finally, all the similarity values are combined to produce a new user’s similarity value. Our experiment results show that our approach is outperformed in cold-start problem compared to the benchmark algorithms. To evaluate whether our system is strong enough to recommend higher accuracy recommendation to users, we also conducted prediction coverage in this research work.

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

Similar content being viewed by others

Notes

  1. https://graph.facebook.com/

  2. https://www.facebook.com/about/pages

  3. https://www.facebook.com/xmenmovies

  4. http://www.imdb.com/

  5. https://www.facebook.com/business/overview

  6. http://www.cnbc.com/id/46227868

References

  1. Adomavicius, G., Tuzhilin, A.: Context-aware Recommender System: Recommender System Handbook. Springer, New York (2011)

    Google Scholar 

  2. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. J. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  3. Aimeur, E., Brassard, G., Fernandez, J.M., Mani Onana, F.S.: Proceedings of the 2006 ACM Symposium on Applied Computing, Dijon, France, pp 872–878 (2006)

  4. Bertini, M., Bimbo, A.D., Ferracani, A., Pezzatini, D.: A social network for video annotation and discovery based on semantic profiling. In: Proceedings of the 21st International Conference Companion on World Wide Web, Lyon, France, pp 317–320 (2012)

  5. Burke, R.: Hybrid recommender systems: survey and experiments. J. User Model. User Adapt. Interact. 12(4), 331–371 (2002)

    Article  MATH  Google Scholar 

  6. Bobadilla, J., Ortega, F., Hernando, A., Bernal, J.: A collaborative filtering approach to mitigate the new user cold start problem. J. Knowl. Based Syst. 26, 225–238 (2012)

    Article  Google Scholar 

  7. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. Proceeding of the 14th Conference on Uncertainty in Artificial Intelligence, Madison, Wisconsin, USA, pp 43–52 (1998)

  8. Chikhaoui, B., Chiazzaro, M., Wang, S.: An improved hybrid recommender system by combining predictions. In: Proceeding of the 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications, Biopolis, Singapore, pp 644–649 (2011)

  9. Cho, Y., Bang, J.: Applying centrality analysis to solve the cold-start and sparsity problems in collaborative filtering. J. Intell. Inf. Syst. 17(3), 99–114 (2011)

    Google Scholar 

  10. Choi, S.M., Han, Y.S.: A content recommendation system based on category correlations. In: Proceeding of the 5th International Multi-Conference on Computing in the Global Information Technology, Valencia, Spain, pp 66–70 (2010)

  11. Chung, K.Y.: Recent trends on convergence and ubiquitous computing. J. Pers. Ubiquitous Comput. (2013). doi:10.1007/s00779-013-0743-2

  12. Chung, K.Y., Na, Y.J., Lee, J.H.: Interactive design recommendation using sensor based smart wear and weather WebBot. J. Wirel. Pers. Commun. 73(2), 243–256 (2013)

    Article  Google Scholar 

  13. Davidson, J., Liebald, B., Liu, J., Nandy, P., Vleet, T.Y., Gargi, U., Gupta, S., He, Y., Lambert, M., Livingston, B., Sampath, D.: The YouTube video recommendation system. In: Proceeding of the 4th ACM Conference on Recommender Systems, Barcelona, Spain, pp 293–296 (2010)

  14. Facebook reports second quarter 2013 results. Facebook, Inc. Retrieved September 23, 2013 from http://investor.fb.com/releasedetail.cfm?ReleaseID=780093

  15. Gunawardana, A., Meek, C.: A unified approach to building hybrid recommender systems. In: Proceeding of the 3rd ACM Conference on Recommender Systems, New York, USA, pp 117–124 (2009)

  16. Ha, I., Oh, K., Jo, G.S.: Personalized advertisement system using social relationship based user modeling. J. Multimed. Tools Appl. (2013). doi:10.1007/s11042-013-1691-6

  17. Haydar, C., Boyer, A., Roussanaly, A.: Hybridising collaborative filtering and trust-aware recommender systems. In: Proceeding of the 8th International Conference on Web Information Systems and Technologies, Porto, Portugal, pp 695–700 (2012)

  18. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. Proceeding of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, USA, pp 230–237 (1999)

  19. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. J. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)

    Article  Google Scholar 

  20. Jin, X., Wang, C., Luo, J., Yu, X., Han, J.: LikeMiner: a system for mining the power of ‘like’ in social media networks. In: Proceeding of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, USA, pp 753–756 (2011)

  21. Jung, H., Chung, K.Y.: Mining based associative image filtering using harmonic mean. J. Clust. Comput. (2013). doi:10.1007/s10586-013-0318-z

  22. Kim, H.N., Alkhaldi, A., El-Saddik, A., Jo, G.S.: Collaborative user modeling with user-generated for social recommender systems. J. Expert Syst. Appl. 38(7), 8488–8496 (2011)

    Article  Google Scholar 

  23. Kim, H.N., El-Saddik, A., Jo, G.S.: Collaborative error-reflected models for cold-start recommender systems. J. Decis. Support Syst. 51(3), 519–531 (2011)

    Article  Google Scholar 

  24. Kim, K.R., Moon, N.M.: Recommender system design using movie genre similarity and preferred genres in Smartphone. J. Multimed. Tools Appl. 61(1), 87–104 (2012)

    Article  MathSciNet  Google Scholar 

  25. Kaminskas, M., Ricci, F.: Contextual music information retrieval and recommendation: state of the art and challenges. J. Comput. Sci. Rev. 6(2–3), 89–119 (2012)

    Article  Google Scholar 

  26. Koren, Y., Robert, Bell: Advances in Collaborative Filtering: Recommender Systems Handbook. Springer, New York (2011)

  27. Lam, X.N., Vu, T., Le, T.D., Duong, A.D.: Addressing cold-start problem in recommendation systems. In: Proceeding of the 2nd International Conference on Ubiquitous Information Management and Communication, Suwon, Korea, pp 208–211 (2008)

  28. Lekakos, G., Giaglis, G.M.: Improving the prediction accuracy of recommendation algorithms: approaches anchored on human factors. J. Interact. Comput. 18(3), 410–431 (2006)

    Article  Google Scholar 

  29. Lempel, R.: Recommendation challenges in web media settings. In: Proceeding of the 6th ACM Conference on Recommender Systems, Dublin, Ireland, pp 205–206 (2012)

  30. Li, Q., Kim, B.M.: Clustering approach for hybrid recommender system. In: Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence, Halifax, Canada, pp 33–38 (2003)

  31. Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Proceeding of the 18th National Conference on Artificial Intelligence, Alberta, Canada, pp 187–192 (2002)

  32. Miller, B.N., Konstan, J.A., Riedl, J.: PocketLens: toward a personal recommender system. J. ACM Trans. Inf. Syst. 22(3), 437–476 (2004)

    Article  Google Scholar 

  33. Nielsen: State of the media: the social media report (2011). Retrieved October 16, 2013 from http://cn.nielsen.com/documents/Nielsen-Social-Media-Report_FINAL_090911.pdf

  34. Oh, S.Y., Chung, K.Y.: Target speech feature extraction using non-parametric correlation coefficient. J. Clust. Comput. (2013). doi:10.1007/s10586-013-0284-5

  35. Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. J. Artif. Intell. Rev. Special Issue on Data Mining on the Internet 13(5–6), 393–408 (1999)

    Google Scholar 

  36. Resnick, P., Varian, H.R.: Recommender systems. Mag. Commun. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  37. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, Chapel Hill, USA, pp 175–186 (1994)

  38. Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proceeding of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere, Finland, pp 253–260 (2002)

  39. Snider, M.: Netflix adds personalized profiles to streaming service. Retrieved August 31, 2013 from http://www.usatoday.com/story/tech/personal/2013/08/01/netflix-adds-new-profile-feature/2603675/

  40. Sun, D., Luo, Z., Zhang, F.: A novel approach for collaborative filering to alleviate the new item cold-start problem. In: Proceeding of the 11th International Symposium on Communications and Information Technologies, Hangzhou, China, pp 402–406 (2011)

  41. Van, A.C., Aroyo, L., Brickley, D., Buser, V., Miller, L., Minno, M., Mostarda, M., Palmisano, D., Raimond, Y., Schreiber, G., Siebes, R.: The NoTube Beancounter: aggregating user data for television programme recommendation. Social Data on the Web (SDoW2009) (2009)

  42. You, T., Rosli, A.N., Ha, I., Jo, G.S.: Clustering method based on genre interest for cold-start problem in movie recommendation. J. Intell. Inf. Syst. 19(1), 57–77 (2013)

    Google Scholar 

  43. Zhang, Z.K., Liu, C., Zhang, Y.C., Zhou, T.: Solving the cold-start problem in recommender systems with social tags. Europhys. Lett. 92(2), 28002 (2010)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2012-0005500). This work was also supported by INHA University.

Author information

Authors and Affiliations

  1. Department of Information Engineering, Inha University, 100 Inha-ro, Nam-gu, Incheon, Republic of Korea

    Ahmad Nurzid Rosli, Tithrottanak You & Inay Ha

  2. School of Computer Information Engineering, Sangji University, Usan-dong, Wonju-si, Gangwon-do, 220-702, Republic of Korea

    Kyung-Yong Chung

  3. School of Computer & Information Engineering, Inha University, 100 Inha-ro, Nam-gu, Incheon, Republic of Korea

    Geun-Sik Jo

Authors
  1. Ahmad Nurzid Rosli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tithrottanak You
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Inay Ha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kyung-Yong Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Geun-Sik Jo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Geun-Sik Jo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rosli, A.N., You, T., Ha, I. et al. Alleviating the cold-start problem by incorporating movies facebook pages. Cluster Comput 18, 187–197 (2015). https://doi.org/10.1007/s10586-014-0355-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-014-0355-2

Keywords

Search

Navigation