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DRARS, A Dynamic Risk-Aware Recommender System

Abstract : The vast amount of information generated and maintained everyday by information systems and their users leads to the increasingly important concern of overload information. In this context, traditional recommender systems provide relevant information to the users. Nevertheless, with the recent dissemination of mobile devices (smartphones and tablets), there is a gradual user migration to the use of pervasive computing environments. The problem with the traditional recommendation approaches is that they do not utilize all available information for producing recommendations. More contextual parameters could be used in the recommendation process to result in more accurate recommendations. Context-Aware Recommender Systems (CARS) combine characteristics from context-aware systems and recommender systems in order to provide personalized recommendations to users in ubiquitous environments. In this perspective where everything about the user is dynamic, his/her content and his/her environment, two main issues have to be addressed: i) How to consider content dynamicity? and ii) How to avoid disturbing the user in risky situations?. In response to these problems, we have developed a dynamic risk sensitive recommen- dation system called DRARS (Dynamic Risk-Aware Recommender System), which model the context-aware recommendation as a bandit problem. This system combines a content-based technique and a contextual bandit algorithm. We have shown that DRARS improves the Upper Condence Bound (UCB) policy, the currently available best algorithm, by calculating the most optimal exploration value to maintain a trade-o between exploration and exploitation based on the risk level of the current user's situation. We conducted experiments in an industrial context with real data and real users and we have shown that taking into account the risk level of users' situations signi cantly increased the performance of the recommender systems.
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Contributor : Djallel Bouneffouf <>
Submitted on : Sunday, July 20, 2014 - 10:55:49 AM
Last modification on : Friday, October 23, 2020 - 4:45:53 PM
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  • HAL Id : tel-01026136, version 1


Djallel Bouneffouf. DRARS, A Dynamic Risk-Aware Recommender System. Computation and Language [cs.CL]. Institut National des Télécommunications, 2013. English. ⟨tel-01026136⟩



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