Analyzing Tagged Resources for Social Interests Detection

Social networks provide an environment of information exchange. This environment reflects user’s opinions, tastes, preferences, interests, etc. We focus on analyzing user’s interests which are key elements for improving adaptation (recommendation, personalization, etc.). In this article, we are interested to overcome problems affected the adaptation quality in social networks, such as the accuracy of the user’s 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 behaviour of the user in order to figure out the tags which really reflect the resources content. We focus on the semi-structured data (resources), since they provide more comprehensible information. Our approach has been tested and evaluated in the Delicious social database. A comparison between our approach and the tag-based approach shows that our approach performs better.

[1]  Giovanni Quattrone,et al.  A query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy , 2010, User Modeling and User-Adapted Interaction.

[2]  Tsvi Kuflik,et al.  Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) : 27th October 2011, Chicago, IL, USA , 2011 .

[3]  Ryen W. White,et al.  Predicting user interests from contextual information , 2009, SIGIR.

[4]  Abdulmotaleb El-Saddik,et al.  Collaborative user modeling with user-generated tags for social recommender systems , 2011, Expert Syst. Appl..

[5]  Corinne Amel Zayani,et al.  An Adaptive Navigation Method for Semi-structured Data , 2012, ADBIS.

[6]  Faïez Gargouri,et al.  A user profile modelling using social annotations: a survey , 2012, WWW.

[7]  Yang Song,et al.  Automatic tag recommendation algorithms for social recommender systems , 2011, ACM Trans. Web.

[8]  Joemon M. Jose,et al.  Personalizing Web Search with Folksonomy-Based User and Document Profiles , 2010, ECIR.

[9]  Analía Amandi,et al.  Hybrid Content and Tag-based Profiles for Recommendation in Collaborative Tagging Systems , 2008, 2008 Latin American Web Conference.

[10]  Florence Sèdes,et al.  Towards an Adaptation of Semi-structured Document Querying , 2007, CIR.

[11]  Rémy Cazabet,et al.  Detection of Overlapping Communities in Dynamical Social Networks , 2010, 2010 IEEE Second International Conference on Social Computing.

[12]  Alexandros Nanopoulos,et al.  Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions , 2010, Artificial Intelligence Review.

[13]  Alberto Córdoba,et al.  An Algorithm for the Improvement of Tag-based Social Interest Discovery , 2010 .

[14]  Faïez Gargouri,et al.  An Extended Architecture for Adaptation of Social Navigation , 2012, WEBIST.

[15]  Yi Zeng,et al.  User Interests Modeling Based on Multi-source Personal Information Fusion and Semantic Reasoning , 2011, AMT.

[16]  Florence Sèdes,et al.  A community-based algorithm for deriving users’ profiles from egocentrics networks: experiment on Facebook and DBLP , 2012, Social Network Analysis and Mining.