Personalization through Query Explanation and Document Adaptation

Volume : (3), Issue : 211, November - 2018

Abstract : We present a new formal approach to retrieval personalization which emcompasses a query personalization process at the user’s side with a light document adaptation at the information server’s side. Our solution relies on the use of a domain ontology: queries and documents are in fact indexed by sets of concepts. For each concept of the query, the query personalization process allows to express the importance of linked concepts, which may vary according to the search context. Each query concept can be ”clarified” by this process; although the proposed method clarifies only central query concepts. The initial query as well as its defined clarifications are sent to the server. Then, the server reconsiders its document representations based on both the query and the concepts clarifications it received. The proposed solution does not require that the information server maintains any user profile, and can be useful when, for privacy concerns, it is committed not to profiling the users.

Article: Download PDF Journal DOI : 2364/2018

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Article No : 10044

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References :

1.  M. W. Berry, Z. Drmac, and E. R. Jessup. Matrices, vector spaces, and information retrieval. SIAM Rev., 41(2):335–362, 1999.
2. G. Bordogna and G. Pasi. Personalised indexing and retrieval of heterogeneous structured documents. Information Retrieval, 8(2):301–318, 2005.
3. P.-A. Chirita, C. S. Firan, and W. Nedjl. Personalized query expansion for the web. In SIGIR, pages 7–14, New-York, 2007.
4. L. P. Dinu. On the classification and aggregation of hierarchies with different constitutive elements. Fundam. Inf., pages 39–50, 2002.
5. J. Euzenat and P. Shvaiko. Ontology matching. Springer-Verlag, Heidelberg (DE), 2007.
6. A. G´omez P´erez, M. Fern´andez, and O. Corcho. Ontological Engineering. Springer Verlag, London, 2004.
7. V. Kashyap and A. Sheth. Semantic and schematic similarities between database objects: a context-based approach. The VLDB Journal, 5(4):276–304, 1996.
8. C. Keßler, M. Raubal, and K. Janowicz. The effect of context on semantic similarity measurement. In OTM Workshops (2), pages 1274–1284, 2007.
9. P. Mylonas, D. Vallet, P. Castells, M. Fernandez, and Y. Avrithis. Personalized informtion retrieval based on context and ontological knowledge. The Knowledge Engineering Review, 23(1):73–100, 2008.
10. J.-Y. Nie and F. Jin. Integrating logical operators in query expansion invector space model. In SIGIR workshop on Mathematical and Formal methods in Information Retrieval, 2002.
11. P. Resnik. Using information content to evaluate semantic similarity in a taxonomy. In IJCAI, pages 448–453, 1995.
12. J. Teeman, S. T. Dumais, and D. J. Liebing. To personalize or not to personalize: Modeling queries with variations in user intent. In SIGIR, pages 163–170, Singapore, 2008.
13. A. Tversky. Features of similarity. Psychological Review, 84(4):327–352, July 1977.
14. A. Ventresque, S. Cazalens, P. Lamarre, and P. Valduriez. Improving interoperability using query interpretation in semantic vector spaces. In ESWC, pages 539–553, 2008.
15. A. Ventresque, T. Cerqueus, L.A. Celton, G. Hervouet, D. Levin, P. Lamarre, and S. Cazalens. Mysins : make your semantic information system. In EGC, pages 629–630, 2010.
16. W. Woods. Conceptual indexing: A better way to organize knowledge. Technical report, Sun Microsystems Laboratories, April 1997.
17. Z. Wu and M. Palmer. Verb semantics and lexical selection. In ACL, pages 133–138, Las Cruces, New Mexico, 1994.