Azim Roussanaly

Local Trust Versus Global Trust Networks in Subjective Logic

Social web permits users to acquire information from anonymous people around the world. This leads to a serious question about the trustworthiness of information and sources. During the last decade, numerous models were proposed to model social trust in the service of social web. Trust modeling follows two main axes, local trust (trust between pair of users), and global trust (users reputation within the community). Subjective logic, is an extension of probabilistic logic that deals with the cases of lack of evidences. An elaborated local trust model based on subjective logic already exists. The aim of this work is to apply this model to the first time on a real data set. Then, we propose another global trust model based also on subjective logic. We apply both models on a real data set of a question answering social network that aims to assist people to find solutions to their technical problems in various domains. Our proposed global trust model ensures a better performance thanks to its precise interpretation of the context of trust, and its ability to satisfy new arrived users.

User Semantic Preferences for Collaborative Recommendations

Personalized recommender systems provide relevant items to users from huge catalogue. Collaborative filtering (CF) and content-based (CB) filtering are the most widely used techniques in personalized recommender systems. CFuses only the user-rating data to make predictions, while CB filtering relies on semantic information of items for recommendation. In this paper we present a new approach taking into account the semantic information of items in a CF system. Many works have addressed this problem by proposing hybrid solutions. In this paper, we present another hybridization technique that predicts users ‘preferences for items based on their inferred preferences for semantic information. With this aim, we propose a new approach to build user semantic profile to model users’ preferences for semantic information of items. Then, we use this model in a user-based CF algorithm to calculate the similarity between users. We apply our approach to real data, the MoviesLens dataset, and compare our results to standards user-based and item-based CF algorithms.

Learning Resource Recommendation: An Orchestration of Content-Based Filtering, Word Semantic Similarity and Page Ranking

Technologies supporting online education have been abundantly developed recent years. Many repositories of digital learning resources have been set up and many recommendation approaches have been proposed to facilitate the consummation of learning resources. In this paper, we present an approach that combines three recommendation technologies: content-based filtering, word semantic similarity and page ranking to make resource recommendations. Content-based filtering is applied to filter syntactically learning resources that are similar to user profile. Word semantic similarity is applied to consolidate the content-based filtering with word semantic meanings. Page ranking is applied to identify the importance of each resource according to its relations to others. Finally, a hybrid approach that orchestrates these techniques has been proposed. We performed several experiments on a public learning resource dataset. Results on similarity values, coverage of recommendations and computation time show that our approach is feasible.

Analyzing Recommender System’s Performance Fluctuations across Users

Recommender systems (RS) are designed to assist users by recommending them items they should appreciate. User based RS exploit users behavior to generate recommendations. As a matter of fact, RS performance fluctuates across users. We are interested in analyzing the characteristics and behavior that make a user receives more accurate/inaccurate recommendations than another.

Studying Relations Between E-learning Resources to Improve the Quality of Searching and Recommendation

Searching and recommendation are basic functions that effectively assist learners to approach their favorite learning resources. Several searching and recommendation techniques in the Information Retrieval (IR) domain have been proposed to apply in the Technology Enhanced Learning (TEL) domain. However, few of them pay attention on particular properties of e-learning resources, which potentially improve the quality of searching and recommendation. In this paper, we propose an approach that studies relations between e-learning resources, which is a particular property existing in online educational systems, to support resource searching and recommendation. Concretely, we rank e-learning resources based on their relations by adapting the Googles PageRank algorithm. We integrate this ranking into a text-matching search engine to refine the search results. We also combine it with a content-based recommendation technique to compute the similarity between user profile and e-learning resources. Experimental results on a shared dataset showed the efficiency of our approach.

Rocchio Algorithm to Enhance Semantically Collaborative Filtering

Recommender system provides relevant items to users from huge catalogue. Collaborative filtering and content-based filtering are the most widely used techniques in personalized recommender systems. Collaborative filtering uses only the user-ratings data to make predictions, while content-based filtering relies on semantic information of items for recommendation. Hybrid recommendation system combines the two techniques. In this paper, we present another hybridization approach: User Semantic Collaborative Filtering. The aim of our approach is to predict users preferences for items based on their inferred preferences for semantic information of items. In this aim, we design a new user semantic model to describe the user preferences by using Rocchio algorithm. Due to the high dimension of item content, we apply a latent semantic analysis to reduce the dimension of data. User semantic model is then used in a user-based collaborative filtering to compute prediction ratings and to provide recommendations. Applying our approach to real data set, the MoviesLens 1M data set, significant improvement can be noticed compared to usage only approach, content based only approach.

User Semantic Model for Dependent Attributes to Enhance Collaborative Filtering

Recommender system provides relevant items to users from huge catalogue. Collaborative filter-ing and content-based filtering are the most widely used techniques in personalized recommender systems. Collaborative filtering uses only the user-ratings data to make predictions, while content-based filtering relies on semantic information of items for recommendation. Hybrid recommenda-tion system combines the two techniques. The aim of this work is to introduce a new approach for semantically enhanced collaborative filtering. Many works have addressed this problem by proposing hybrid solutions. In this paper, we present another hybridization technique that pre-dicts users preferences for items based on their inferred preferences for semantic information of items. For this, we design a new user semantic model by using Rocchio algorithm and we apply a latent semantic analysis to reduce the dimension of data. Applying our approach to real data, the MoviesLens 1M dataset, significant improvement can be noticed compared to usage only approach, and hybrid algorithm.