Filtrage collaboratif sensible au contexte

Filtrage collaboratif sensible au contexte

Josiane Mothe Ambinintsoa Jocelyn Rakotonirina 

ESPE, Université de Toulouse, Université de Toulouse Jean Jaurès IRIT, UMR 5505 CNRS, 118 Route de Narbonne, Toulouse, France

DMI, Université d’Antananarivo, Madagascar Mathématiques Informatique et Statistique Appliquées, MISA BP 906 Ankatso

Corresponding Author Email: 
Josiane.Mothe@irit.fr; Ambinintsoa26@outlook.com
Page: 
89-109
|
DOI: 
https://doi.org/10.3166/ISI.23.1.89-109
Received: 
|
Accepted: 
|
Published: 
28 February 2018
| Citation
Abstract: 

Recommender systems are designed to provide user with items related to their ongoing browsing and that may be of interest to them. User interest depends on the context. In this work, we propose a hybrid CBCF (Context-aware Based Collaborative Filtering) system combining context-sensitive and collaborative filtering. We define context as the objective or intent of the user. We model it by a LDA (Latent Dirichlet Allocation) approach which generates a topic model for each intention. We evaluated our approach using the Book-Crossing collection and demonstrated the superiority of our model over several state-of-the-art methods.

Keywords: 

information systems, information retrieval, recommender systems, latent dirichlet allocation, collaborative filtering, hybrid recommender system

1. Introduction
2. État de l’art
3. Filtrage collaboratif basé sur LDA
4. Evaluation et résultats
5. Conclusion
  References

Adomavicius G., Tuzhilin A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, vol. 17, n° 6, p. 734-749.

Adomavicius G., Tuzhilin A. (2011). Contextaware recommender systems. Recommender systemshandbook, p. 217-253. Springer.

Alghamdi R. and Alfalqi, K. (2015). A survey of topic modeling in text mining. International Journal of Advanced Computer Science and Applications (IJACSA), vol. 6, n° 1.

Bell R. M., Koren Y., and Volinsky, C. (2008). The bellkor 2008 solution to the Netflix prize. Statistics Research Department at AT&T Research.

Berry M. J. and Lino, G. (1997). Data mining techniques : for marketing, sales, and customer support. John Wiley & Sons, Inc.

Billsus D. and Pazzani M. J. (2000). User modeling for adaptive news access. User modeling and user-adapted interaction, vol. 10, n° 2-3, p. 147-180.

Blei D. M., Ng A. Y., Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of machine Learning research, vol. 3 (January), p. 993-1022.

Blei D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4):77-84.

Bobadilla J., Ortega F., Hernando A., and Gutièrrez A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, p. 109-132.

Borras J., Moreno A., Valls A. (2014). Intelligent tourism recommender systems: A survey. Expert Systems with Applications, vol. 41, n° 16, p. 7370-7389.

Buntine W. (2009). Estimating likelihoods for topic models. Asian Conference on Machine Learning, p. 51-4. Springer.

Burke R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, vol. 12, n° 4 331-370.

Burke R. (2007). Hybrid web recommender systems. The Adaptive Web, Springer, p. 377-408.

Candillier L., Chevalier M., Dudognon D., Mothe J. (2012). Multiple Similarities for Diversity in Recommender Systems.International Journal on Advances in Intelligent Systems, International Academy, Research and Industry Association, vol. 5, n° 3&4, p. 234-246.

Candillier L., Chevalier M., Dudognon D., Mothe J. (2011). Diversity in Recommender Systems: Bridging the gap between users and systems, International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services, CENTRIC 2011, p. 48-58.

Canut M.-F., On-At S., Péninou A., Sèdes F. (2015). Enrichissement du profil utilisateur à partir de son réseau social dans un contexte dynamique : application d’une méthode de pondération temporelle. INFormatique des Organisations et Systèmes d’Information et de Décision, INFORSID’15, p. 15-30.

Chaker H., Chevalier M., Tricot A. (2013). Une approche de gestion de contextes métiers pour l’accès à l’information. INFormatique des Organisations et Systèmes d’Information et de Décision, INFORSID’13, p. 115-130.

Chevalier M., Dudognon D., Mothe J. (2016). ADORES: a diversity-oriented online recommender system. Proceedings of the 31st Annual ACM Symposium on Applied Computing, p. 1075-1076..

Dey A. K. (2001). Understanding and using context. Personal and ubiquitous computing, vol. 5, n° 1, p. 4-7.

Dudognon D. (2014). Diversité et système de recommandation : application à une plateforme de blogs à fort trafic (convention CIFRE n 20091274). Thèse de doctorat, Université de Toulouse, Université Toulouse III-Paul Sabatier.

Ekstrand M. D., Riedl, J. T., Konstan, J. A., et al. (2011). Collaborative filtering recommender systems. Foundations and Trends R in Human – Computer Interaction, vol. 4, n° 2, p. 81-173.

Fei-Fei L., Perona, P. (2005). A bayesian hierarchical model for learning natural scene categories. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 2, p. 524-531.

Griffths T. L., Steyvers M. (2004). Finding scientific topics. Proceedings of the National academy of Sciences, 101(suppl 1), p. 5228-5235.

Herlocker J., Konstan J. A., Riedl J. (2002). An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms, Information Retrieval, vol. 5, n° 4, p. 287-310.

Herlocker J.L., Konstan J.A., Terveen L. G., Riedl J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), vol. 22, n° 1, p. 5-53.

Ionescu R. T., Chifu A. G., Mothe J. (2015). DeShaTo: describing the shape of cumulative topic distributions to rank retrieval systems without relevance judgments. In International Symposium on String Processing and Information Retrieval, p. 75-82. Springer International Publishing.

Konstan J. A., Miller B. N., Maltz D., Herlocker J. L., Gordon L. R., Riedl J. (1997). Grouplens : applying collaborative filtering to UseNet news. Communications of the ACM, vol. 40, n° 3, p. 77-87.

Krestel R., Fankhauser P., Nejdl W. (2009). Latent Dirichlet Allocation for tag recommendation. Proceedings of the third ACM conference on Recommender systems, p. 61-68.

Lamsfus C., Alzua-Sorzabal A., Martin D., Salvador Z., Usandizaga A. (2009). Human-centric ontology-based context modelling in tourism. KEOD, p. 424-434.

Louëdec J., Chevalier M., Garivier A., Mothe J. (2015). Systèmes de recommandation et algorithmes de bandits: Notebook Ipython. Tutoriel. http://www.math.univ-toulouse.fr/~jlouedec/demoBandits.html

Louëdec J., Chevalier M., Mothe J., Garivier A., Gerchinovitz S. (2015). A multiple-play bandit algorithm applied to recommender systems. FLAIRS Conference, p. 67-72.

Louëdec J., Chevalier M., Garivier A., Mothe J. (2015). Algorithmes de bandits pour la recommandation à tirages multiples.Document numérique, Hermès, vol. 18, n° 2&3, p. 59-79.

Mothe J., Rakotonirina A.J. (2017). Filtrage collaboratif sensible au contexte - Une approche basée sur LDA. INFormatique des Organisations et Systèmes d’Information et de Décision (INFORSID), p. 113-126.

Mothe J., Ramiandriosa F., Rasolomanana M. (2018). Automatic Keyphrase Extraction using Graph-based Methods. ACM Symposium on Applied Computing (SAC).

Mobasher B., Jin X., Zhou Y. (2004). Semantically enhanced collaborative filltering on the web. In Web Mining: From Web to Semantic Web, p. 57-76. Springer.

Nguyen C. P. (2010). Conception d’un système d’apprentissage et de travail pervasif et adaptatif fondé sur un modèle de scénario. Thèse de doctorat.

Nilashi M., Bin Ibrahim O., Ithnin N. (2014). Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Systems with Applications, vol. 41, n° 8, p. 3879-3900.

Palmisano C., Tuzhilin A., Gorgoglione M. (2008). Using context to improve predictive modeling of customers in personalization applications. IEEE transactions on knowledge and data engineering, vol. 20, n° 11, 1535-1549.

Pazzani M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, vol. 13, n° 5-6, p. 393-408.

Pazzani M. J., Billsus D. (2007). Content-based recommendation systems. The Adaptive Web, Springer, p. 325-341.

Picot-Clémente R. (2011). Une architecture générique de Systèmes de recommandation de combinaison d’items : application au domaine du tourisme. Thèse de doctorat, Université de Bourgogne.

Pinel-Sauvagnat K., Mothe J. (2013). Mesures de la qualité des systèmes de recherche d’information. Dans : Ingénierie des Systèmes d’Information, Hermès Science, Numéro spécial Evaluation des systèmes d’information, Hors Série, n° 3, p. 11-38.

Ramage D., Hall D., Nallapati R., Manning C. D. (2009). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 1, p. 248-256. Association for Computational Linguistics.

Rosen-Zvi M., Griths T., Steyvers M., Smyth P. (2004). The author-topic model for authors and documents. Proceedings of the 20th Conference on Uncertainty in artificial intelligence, p. 487-494. AUAI Press.

Rakotonirina A. J. (2017). Filtrage Collaboratif Sensible au Contexte : une approche basée sur LDA, thèse de Master.

Ryan N., Pascoe J., Morse D. (1999). Enhanced reality fieldwork: the context aware archaeological assistant. Bar International Series, 750, p. 269-274.

Salton G., McGill, M. J. (1986). Introduction to modern information retrieval, Salton, G. (1989). Automatic text processing: The transformation, analysis, and retrieval of. Reading. Addison-Wesley.

Sarwar B., Karypis G., Konstan J., Riedl J. (2001). Itembased collaborative filtering recommendation algorithms. Proceedings of the 10th International ACM Conference on World Wide Web, p. 285-295.

Schein A. I., Popescul, A., Ungar, L. H., Pennock D. M. (2002). Methods and metrics for cold-start recommendations. Proceedings of the 25th Annual International ACM SIGIR conference on Research and development in Information Retrieval, p. 253-260.

Si X., Sun M. (2009). Tag-LDA for scalable real-time tag recommendation. Journal of Computational Information Systems, vol. 6, n° 1, p. 23-31.

Smyth B., Cotter P. (2000). A personalized television listings service. Communications of the ACM, 43, n° 8, p. 107-111.

Steyvers M., Smyth P., Rosen-Zvi M., Grifiths T. (2004). Probabilistic author-topic models for information discovery. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, p. 306-315..

Sullivan D. O., Smyth B., Wilson D. (2004). Preserving recommender accuracy and diversity in sparse datasets. International Journal on Artificial Intelligence Tools, vol. 13, n° 1, p. 219-235.

Tavakol M., Brefeld U. (2014). Factored mdps for detecting topics of user sessions. Proceedings of the 8th ACM Conference on Recommender Systems, p. 33-40.

Wallach H. M., Mimno D. M., McCallum A. (2009). Rethinking LDA: Why priors matter. Advances in neural information processing systems, p. 1973-1981.

Wei X., Croft W.B. (2006). LDA-based document models for ad-hoc retrieval. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, p. 178-185.

Wilson D. C., Smyth B., and Sullivan D. O. (2003). Sparsity reduction in collaborative recommendation: A case-based approach. International journal of pattern recognition and artificial intelligence, vol. 17, n° 5, p. 863-884.

Xie W., Dong Q., Gao H. (2014). A probabilistic recommendation method inspired by Latent Dirichlet Allocation model. Mathematical Problems in Engineering.

Yu K., Schwaighofer A., Tresp V., Xu X., Kriegel H.-P. (2004). Probabilistic memory-based collaborative filtering. IEEE Transactions on Knowledge and Data Engineering, vol. 16, n° 1, p. 56-69.

Yu K., Zhang B., Zhu H., Cao H., Tian J. (2012). Towards personalizedcontext-aware recommendation by mining context logs through topic models. Pacific-Asia Conference on Knowledge Discovery and Data Mining, p. 431-443.

Yuan J., Gao F., Ho Q., Dai W., Wei J., Zheng X., Xing E. P., Liu T.-Y., Ma W.-Y. (2015). LightLDA: Big topic models on modest computer clusters. Proceedings of the 24th International ACM Conference on World Wide Web, p. 1351-1361.