Anne Boyer

Densifying a behavioral recommender system by social networks link prediction methods

Recommender systems are widely used for personalization of information on the Web and information retrieval systems. collaborative filtering (CF) is the most popular recommendation technique. However, classical CF (CCF) systems use only direct links and common features to model relationships between users. This paper presents a new densified behavioral network based collaborative filtering model (D-BNCF), based on the BNCF approach that uses navigational patterns to model relationships between users. D-BNCF exploits additionally social networks techniques, such as prediction link methods, to discover new links throughout the behavioral network. The final aim is the involvement of these new links in prediction generation to improve the quality of recommendations. The approach proposed is evaluated in terms of accuracy on a real usage dataset. The experimentation shows the benefit of exploiting new links to compute predictions as a high precision is reached. Besides, the evaluation of a combined model (that exploits the more accurate D-BNCF models) shows also the interest of combining similarities based on two different link prediction methods and its impact on the accuracy of high predictions.

From Social Networks to Behavioral Networks in Recommender Systems

Recommender systems are widely used for personalization of information on the web and information retrieval systems. Collaborative Filtering (CF) is the most popular recommendation technique. However, classical CF systems use only direct links and common features to model relationships between users. This paper presents a new Collaborative Filtering approach (BNCF) based on a behavioral network that uses navigational patterns to model relationships between users and exploits social networks techniques, such as transitivity, to explore additional links throughout the behavioral network. The final aim consists in involving these new links in prediction generation, to improve recommendations quality. BNCF is evaluated in terms of accuracy on a real usage dataset. The experimentation shows the benefit of exploiting new links to compute predictions. Indeed, BNCF highly improves the accuracy of predictions, especially in terms of HMAE.

A low-order markov model integrating long-distance histories for collaborative recommender systems

Recommender systems provide users with pertinent resources according to their context and their profiles, by applying statistical and knowledge discovery techniques. This paper describes a new approach of generating suitable recommendations based on the active users navigation stream, by considering long and short-distance resources in the history with a tractable model. The Skipping Based Recommender we propose uses Markov models inspired from the ones used in language modeling while integrating skipping techniques to handle noise during navigation. Weighting schemes are also used to alleviate the importance of distant resources. This recommender has also the characteristic to be anytime. It has been tested on a browsing dataset extracted from Intranet logs provided by a French bank. Results show that the use of exponential decay weighting schemes when taking into account non contiguous resources to compute recommendations enhances the accuracy. Moreover, the skipping variant we propose provides a high accuracy while being less complex than state of the art variants.

Modeling Preferences in a Distributed Recommender System

A good way to help users finding relevant items on document platforms consists in suggesting content in accordance with their preferences. When implementing such a recommender system, the number of potential users and the confidential nature of some data should be taken into account. This paper introduces a new P2P recommender system which models individual preferences and exploits them through a user-centered filtering algorithm. The latter has been designed to deal with problems of scalability, reactivity, and privacy.

Personalized communities in a distributed recommender system

The amount of data exponentially increases in information systems and it becomes more and more difficult to extract the most relevant information within a very short time. Among others, collaborative filtering processes help users to find interesting items by modeling their preferences and by comparing them with users having the same tastes. Nevertheless, there are a lot of aspects to consider when implementing such a recommender system. The number of potential users and the confidential nature of some data are taken into account. This paper introduces a new distributed recommender system based on a user-based filtering algorithm. Our model has been transposed for Peer-to-Peer architectures. It has been especially designed to deal with problems of scalability and privacy. Moreover, it adapts its prediction computations to the density of the user neighborhood.

Detecting Leaders in Behavioral Networks

The development of the Web engendered the emergence of virtual communities. Analyzing information flows and discovering leaders through these communities becomes thus, a major challenge in different application areas. In this paper, we present an algorithm that aims at detecting leaders in the context of behavioral networks. This algorithm considers the high connectivity and the potentiality of propagating accurate appreciations so as to detect reliable leaders through these networks. This approach is evaluated in terms of precision using a real usage dataset. The results of the experimentation show the interest of our approach to detect TopN behavioral leaders that predict accurately the preferences of the other users. Besides, our approach can be harnessed in different application areas caring about the role of leaders.

Enhancing collaborative filtering by frequent usage patterns

Recommender systems contribute to the personalization of resources on the Web sites and information retrieval systems. In this paper, we present a hybrid recommender system using a user based approach which combines predictions based on Web usage patterns and rating data. We suggest a new technique that takes into account frequent patterns in order to compute correlations between users and select neighbors. Then, we combine this technique with collaborative filtering using Pearson correlation metric. The aim of this combination consists in the evaluation of the impact of each technique on recommendations. The performance of our system is tested without and by combining predictions in terms of accuracy and robustness. The different tests show that the more the navigational based technique is involved in the recommendation process, the more the best predictions are accurate and the system is robust.

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.

Learning of mediation strategies for heterogeneous agents cooperation

Making heterogeneous agents cooperate is still an open problem. We have studied the interaction between a human agent and an information service agent. Our approach is to introduce a mediator agent to formalize the requests of the users, according to their profile and then to give the relevant answers. The mediator must find the best mediation strategy (a sequence of interactions) with a Markov decision process (MDP). The states are built on an attribute based referential and the capacity of the source to answer the request under formalization. The actions allow to ask questions to the user or to probe the information source. The rewards reflect the satisfaction of the user, the length of the mediation and the quantity of results. Our prototype uses reinforcement learning (Q-learning) for an on-line adaptation without requiring an a priori model. We describe our experiments on a flight information service with a simulated behaviour.

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.