Malek Ghenima

Personalized Information Retrieval: Application to Virtual Communities

Internet has become the largest library through the history of humanity. Having such a big library made the search process more complicated. In fact, traditional search engines answer users by sending back the same results to different users having expressed different information needs and different preferences. A significant part of difficulties [1],[4] is due to vocabulary problems (polysemy, synonymy...). Such problems trigger a strong need for personalizing the search results based on user preferences. The goal of personalized information [11] is to generate meaningful results to a collection of information users that may interest them using user’s profile. This paper presents a personalized information retrieval approach based on user profile. User profile is built from the acquisition of explicit and implicit user data. The proposed approach also presents a semantic-based optimization method for user query. The system uses user profile to construct virtual communities. Moreover, it uses the user’s navigation data to predict user’s preferences in order to update virtual communities.

Utilizing Virtual Communities for Information Retrieval and User Modeling

Internet has become the largest library in human history. Having such a large library made the search process more complicated. In fact, traditional search engines respond users by sending back the same results to different users having expressed different information needs and different preferences. A significant part of difficulties,report to vocabulary problems (polysemy, synonymy...). Such problems trigger a strong need to personalize the search results based on user preferences. The goal of personalized information is to generate meaningful results interesting to a number of information users using their profile. This paper presents a personalized information retrieval approach based on user profile. User profile is built from the acquisition of explicit and implicit user data. The proposed approach also presents a semantic-based optimization method for user query. The system uses user profile to construct virtual communities. Moreover, it uses the user’s navigation data to predict user’s preferences in order to update virtual communities.