Abstract
A large body of work in the information retrieval area has highlighted that relevance is a complex and a challenging concept. The underlying complexity appears mainly from the fact that relevance is estimated by considering multiple dimensions and that most of them are subjective since they are user-dependent. While the most used dimension is topicality, recent works risen particularly from personalized information retrieval have shown that personal preferences and contextual factors such as interests, location and task peculiarities have to be jointly considered in order to enhance the computation of document relevance. To answer this challenge, the commonly used approaches are based on linear combination schemes that rely basically on the non-realistic independency property of the relevance dimensions. In this paper, we propose a novel fuzzy-based document relevance aggregation operator able to capture the user’s importance of relevance dimensions as well as information about their interaction. Our approach is empirically evaluated and relies on the standard TREC contextual suggestion dataset involving 635 users and 50 contexts. The results highlight that accounting jointly for individual differences toward relevance dimension importance as well as their interaction introduces a significant improvement in the retrieval performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Borlund, P.: The concept of relevance in IR. Journal of the American Society for Information Science and Technology 54(10), 913–925 (2003)
Carterette, B., Kumar, N., Rao, A., Zhu, D.: Simple rank-based filtering for microblog retrieval: Implications for evaluation and test collections. In: Proceedings of the 20th Text REtrieval Conference (2011)
Choquet, G.: Theory of capacities. Annales de l’Institut Fourier 5, 131–295 (1953)
da Costa Pereira, C., Dragoni, M., Pasi, G.: Multidimensional relevance: Prioritized aggregation in a personalized information retrieval setting. Inf. Process. Manage. 48(2), 340–357 (2012)
Daoud, M., Tamine, L., Boughanem, M.: A personalized graph-based document ranking model using a semantic user profile. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 171–182. Springer, Heidelberg (2010)
Daoud, M., Tamine, L., Boughanem, M., Chebaro, B.: Learning implicit user interests using ontology and search history for personalization. In: Weske, M., Hacid, M.-S., Godart, C. (eds.) WISE Workshops 2007. LNCS, vol. 4832, pp. 325–336. Springer, Heidelberg (2007)
Dean-Hall, A., Clarke, C., Kamps, J., Thomas, P., Simone, N., Voorhes, E.: Overview of the trec 2013 contextual suggestion track. In: Text REtrieval Conference (TREC), National Institute of Standards and Technology (NIST) (2013)
Eickhoff, C., de Vries, A.P., Collins-Thompson, K.: Copulas for information retrieval. In: Proceedings of the 36th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland (2013)
Gerani, S., Zhai, C., Crestani, F.: Score transformation in linear combination for multi-criteria relevance ranking. In: Baeza-Yates, R., de Vries, A.P., Zaragoza, H., Cambazoglu, B.B., Murdock, V., Lempel, R., Silvestri, F. (eds.) ECIR 2012. LNCS, vol. 7224, pp. 256–267. Springer, Heidelberg (2012)
Göker, A., Myrhaug, H.: Evaluation of a mobile information system in context. Inf. Process. Manage. 44(1), 39–65 (2008)
Grabisch, M.: Fuzzy integral in multicriteria decision making. Fuzzy Sets and Systems 69(3), 279–298 (1995)
Grabisch, M., Murofushi, T., Sugeno, M., Kacprzyk, J.: Fuzzy Measures and Integrals. Theory and Applications. Physica Verlag, Berlin (2000)
Liu, T.-Y.: Learning to rank for information retrieval. Foundations and Trends in Information Retrieval 3(3), 225–331 (2009)
Mata, F., Claramunt, C.: Geost: geographic, thematic and temporal information retrieval from heterogeneous web data sources. In: Kim, K.-S. (ed.) W2GIS 2011. LNCS, vol. 6574, pp. 5–20. Springer, Heidelberg (2010)
Nagmoti, R., Teredesai, A., De Cock, M.: Ranking approaches for microblog search. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2010, vol. 1, pp. 153–157. IEEE Computer Society, Washington, DC (2010)
Saracevic, T.: Relevance: A review of the literature and a framework for thinking on the notion in information science. part iii: Behavior and effects of relevance. Journal of the American Society for Information Science 58(13), 2126–2144 (2007)
Sieg, A., Mobasher, B., Burke, R.: Web search personalization with ontological user profiles. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, CIKM 2007, New York, NY, USA, pp. 525–534 (2007)
Wolfe, S.R., Zhang, Y.: Interaction and personalization of criteria in recommender systems. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 183–194. Springer, Heidelberg (2010)
Yang, P., Fang, H.: Opinion-based user profile modeling for contextual suggestions. In: Proceedings of the 2013 Conference on the Theory of Information Retrieval, ICTIR 2013, pp. 80–83. ACM (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Moulahi, B., Tamine, L., Yahia, S.B. (2014). Toward a Personalized Approach for Combining Document Relevance Estimates. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-08786-3_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08785-6
Online ISBN: 978-3-319-08786-3
eBook Packages: Computer ScienceComputer Science (R0)