Optimizing an Utility Function for Exploration / Exploitation Trade-off in Context-Aware Recommender System

In this paper, we develop a dynamic exploration/ exploitation (exr/exp) strategy for contextual recommender systems (CRS). Specifically, our methods can adaptively balance the two aspects of exr/exp by automatically learning the optimal tradeoff. This consists of optimizing a utility function represented by a linearized form of the probability distributions of the rewards of the clicked and the non-clicked documents already recommended. Within an offline simulation framework we apply our algorithms to a CRS and conduct an evaluation with real event log data. The experimental results and detailed analysis demonstrate that our algorithms outperform existing algorithms in terms of click-through-rate (CTR)

Improving adaptation of ubiquitous recommander systems by using reinforcement learning and collaborative filtering

The wide development of mobile applications provides a considerable amount of data of all types (images, texts, sounds, videos, etc.). Thus, two main issues have to be considered: assist users in finding information and reduce search and navigation time. In this sense, context-based recommender systems (CBRS) propose the user the adequate information depending on her/his situation. Our work consists in applying machine learning techniques and reasoning process in order to bring a solution to some of the problems concerning the acceptance of recommender systems by users, namely avoiding the intervention of experts, reducing cold start problem, speeding learning process and adapting to the user's interest. To achieve this goal, we propose a fundamental modification in terms of how we model the learning of the CBRS. Inspired by models of human reasoning developed in robotic, we combine reinforcement learning and case-based reasoning to define a contextual recommendation process based on different context dimensions (cognitive, social, temporal, geographic). This paper describes an ongoing work on the implementation of a CBRS based on a hybrid Q-learning (HyQL) algorithm which combines Q-learning, collaborative filtering and case-based reasoning techniques. It also presents preliminary results by comparing HyQL and the standard Q-Learning w.r.t. solving the cold start problem.Comment: arXiv admin note: text overlap with arXiv:1301.435

Proposition d'une technique de gestion de projet dans les startups

This project is part of the development of mobile CRM. It aims to develop a management application client named NOMALYS. This application allows the commercial and business leaders to see their CRM Mobile. We have focused in this project on the techniques of projects management, this study allowed to classify different techniques for managing software projects and proposed the most closely technique that match the needs of the studied company.Comment: in Frenc