Movie recommendations based in explicit and implicit features extracted from the filmtipset dataset

ACM International Conference Proceeding Series

Volumn , Issue , 2010, Pages 45-52

  Díez, Fernando ^a   Chavarriaga, J Enrique ^a   Campos, Pedro G ^a,b   Bellogín, Alejandro ^a  

^a UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

^b UNIVERSIDAD DEL BÍO BÍO   (Chile)

Author keywords

Challenge; Movie recommendations; Random walk; Recommender systems; Social networks

Indexed keywords

CHALLENGE; MOVIE RECOMMENDATIONS; RANDOM WALK; RECOMMENDER SYSTEMS; SOCIAL NETWORKS;

ALGORITHMS; EXPERIMENTS; INFORMATION RETRIEVAL; RANDOM PROCESSES; SOCIAL NETWORKING (ONLINE);

MOTION PICTURES;

EID: 78650103292     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1869652.1869660     Document Type: Conference Paper

References (24)

1. Said, A., Berkovsky, S., De Luca, E. W. Putting Things in Context: Challenge on Context-Aware Movie Recommendation. CAMRa2010: Proceedings of the RecSys '10 Challenge on Context-aware Movie Recommendation, 2010
2. Takács, G.; Pilászy, I.; Németh, B.; Tikk, D. 2009, Scalable Collaborative Filtering Approaches for Large Recommender Systems, Journal of Machine Learning Research 10: 623-656
3. L. Ramaswamy, P. Deepak, R. Polavarapu, K. Gunasekera, D. Garg, K. Visweswariah, S. Kalyanaraman. CAESAR: A Context-Aware, Social Recommender System for Low-End Mobile Devices. Tenth International Conference on Mobile Data Management Systems, Services and Middleware, 2009.
4. X. Amatriain, J. M. Pujol, N. Tintarev, N. Oliver. Rate it Again: Increasing Recommendation Accuracy by User reRating. Proceedings of the 2009 ACM conference on Recommender Systems, 2009.
5. N. Lathia, S. Hailes, L. Capra. kNN CF: A Temporal Social Network. Proceedings of the 2008 ACM conference on Recommender systems, 2008.
6. X. Amatriain, N. Lathia, J. M. Pujol, H. Kwak, N. Oliver. The Wisdom of the few: a collaborative approach based on expert opinions from the web, Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 2009, pp. 532-539.
7. N. Lathia, S. Hailes, L. Capra. Temporal Collaborative Filtering With Adaptive Neighbourhoods. Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 2009, pp. 796-797.
8. T. Q. Lee, Y. Park, Y.-T. Park. An empirical study on effectiveness of temporal information as implicit ratings. Expert Systems with Applications 36:1315-1321, 2009.
9. Y. Koren. Collaborative Filtering with Temporal Dynamics. Proceedings of the 15th ACM SIGKDD international conference on Knowledge Discovery and Data Mining, Paris, France, 2009, pp. 447-456.
10. K. Miyahara and M. J. Pazzani, Improvement of collaborative filtering with the simple Bayesian classifier, Information Processing Society of Japan, vol. 43, no. 11, 2002.
11. X. Su, T. M. Khoshgoftaar. A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence Volume 2009, Article N. 4, 2009.
12. R. J. A. Little, Missing-data adjustments in large surveys, Journal of Business & Economic Statistics, vol. 6, no. 3, pp. 287-296, 1988.
13. D. B. Rubin, Multiple Imputation for Nonresponse in Surveys, John Wiley & Sons, New York, NY, USA, 1987.
14. X. Su, T. M. Khoshgoftaar, and R. Greiner, A mixture imputation-boosted collaborative filter, in Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference (FLAIRS '08), pp. 312-317, Coconut Grove, Fla, USA, May 2008.
15. T. Q. Lee, Y. Park, Y.T. Park. An empirical study on effectiveness of temporal information as implicit ratings. Expert Systems with Applications 36:1315-1321, 2009.
16. O. Celma. Music Recommendation and Discovery in the Long Tail. PhD thesis, Universitat Pompeu Fabra, Barcelona, Spain, 2008.
17. L. Baltrunas, X. Amatriain, Towards Time-Dependant Recommendation based on Implicit Feedback. In Proceedings of the third ACM conference on Recommender systems, New York, USA, 2009, pp. 423-424.
18. Shepitsen, A., Gemmell, J., Mobasher, B., and Burke, R. 2008. Personalized recommendation in social tagging systems using hierarchical clustering. In Proceedings of the 2008 ACM Conference on Recommender Systems (Lausanne, Switzerland, October 23 - 25, 2008). RecSys '08. ACM, New York, NY, 259-266. DOI= http://doi.acm.org/10.1145/1454008.1454048
19. I. Konstas, V. Stathopoulos, J. M. Jose. On Social Networks and Collaborative Recommendation. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, Boston, MA, USA, pp. 195-202. 2009.
20. Ma, H., Yang, H., Lyu, M. R., and King, I. 2008. SoRec: social recommendation using probabilistic matrix factorization. In Proceeding of the 17th ACM Conference on information and Knowledge Management (Napa Valley, California, USA, October 26 - 30, 2008). CIKM '08. ACM, New York, NY, 931-940. DOI= http://doi.acm.org/10.1145/1458082.1458205
21. Bell, R. M., Bennett, J., Koren, Y., and Volinsky, C. 2009. The million dollar programming prize. IEEE Spectr. 46, 5 (May. 2009), 28-33. DOI= http://dx.doi.org/10.1109/MSPEC.2009.4907383
22. Fouss, F., Pirote, A. Randon-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation. IEEE Trans. Knowl. Data Eng., 19(3):355-369, 2007.
23. Liu, F. and Lee, H.J. Use of social network information to enhance collaborative filtering performance. Expert Systems with Appplications, 37(7):4772-4778, 2010.
24. Davis, J. and Goadrich, M. 2006. The relationship between Precision-Recall and ROC curves. In Proceedings of the 23rd international Conference on Machine Learning (Pittsburgh, Pennsylvania, June 25 - 29, 2006). ICML '06, vol. 148. ACM, New York, NY, 233-240. DOI=http://doi.acm.org/10.1145/ 1143844.1143874 (Pubitemid 46966849)