Social media provides an environment of information exchange. They principally rely on their users to create content, to annotate others’ content and to make on-line relationships. The user activities reflect his opinions, interests, etc. in this environment. We focus on analyzing this social environment to detect user interests which are the key elements for improving adaptation. This choice is motivated by the lack of information in the user profile and the inefficiency of the information issued from methods that analyze the classic user behavior (e.g. navigation, time spent on web page, etc.). So, having to cope with an incomplete user profile, the user social network can be an important data source to detect user interests. The originality of our approach is based on the proposal of a new technique of interests` detection by analyzing the accuracy of the tagging behavior of a user in order to figure out the tags which really reflect the content of the resources. So, these tags are somehow comprehensible and can avoid tags “ambiguity” usually associated to these social annotations. The approach combines the tag, user and resource in a way that guarantees a relevant interests detection. The proposed approach has been tested and evaluated in the Delicious social database. For the evaluation, we compare the result issued from our approach using the tagging behavior of the neighbors (the egocentric network and the communities) with the information yet known for the user (his profile). A comparative evaluation with the classical tag-based method of interests detection shows that the proposed approach is better

MCP, Feedback, Relavance

[1] P. L. Stanchev and D. G. Jr, “Current state and research trend in the image database systems,” mathematics and education in Mathematics, Brovoez, pp. 66–76, 2002.
[2] D. F. Long, D. H. Zhang, and P. D. D. Feng, “Fundamental of content based image retrieval,” research Microsoft, 2003.
[3] S. Jain and S.N.Pradhan, “Enhancement of color image retrieval capabilities: function of color with texture(optimized),” NUCON 2007,Nirma University, December 2007.
[4] I. Valova and B. Rache, “Retrieval by color features in image databases,” International Conference on Computer Systems and Technologies - CompSysTech2002, 2002.
[5] I. Valova and B. Rachev, “Image databases an approach for image segmentation and color reduction analysis and synthesis,” International Conference on Computer Systems and Technologies - CompSysTech2003, 2003.
[6] I. Valova, B. Rachev, and M. Vassilakopoulos, “Optimization of the algorithm for image retrieval by color features,” International Conference on Computer Systems and Technologies - CompSysTech, 2006.
[7] R. M. Hralick, “Statistical and structural approaches to texture,” IEEE .67, p. 786805, 1979.
[8] M. Amadasun and R. King, “Texural features corresponding to texural properties,” IEEE Transaction on system, Man and Cybernatics, 1989.
[9] S. C. Hoi, M. R. Lyu, and R. Jin, “A unified log-based relevance feedback scheme for image retrieval,” IEEE Transaction on knowledge and data engineering, Vol. 18, No. 4,, April 2006.
[10] Tesic.J. and M. B, “Nearest neighbour search for relevance feedback. in computer vision and pattern recognition,” IEEE Computer Society Conference, Volume 2,pp. 18–20, June 2003.