Mobile Context-Aware Recommender Systems can be naturally modelled as an exploration exploitation trade-off (exr exp) problem, where the system has to choose between maximizing its expected rewards dealing with its current knowledge (exploitation) and learning more about the unknown user's preferences to improve its knowledge (exploration). This problem has been addressed by the reinforcement learning community but they do not consider the risk level of the current user's situation, where it may be dangerous to recommend items the user may not desire in her current situation if the risk level is high. We introduce in this paper an algorithm named R-UCB that considers the risk level of the user's situation to adaptively balance between exr and exp. The detailed analysis of the experimental results reveals several important discoveries in the exr exp behaviour.
Few research works are dedicated to study MCARS. @cite_16 proposes a method which consists of building a dynamic user's profile based on time and user's experience. The user's preferences in the user's profile are weighted according to the situation (time, location) and the user's behavior. To model the evolution on the user's preferences according to his temporal situation in different periods (like workday or vacations), the weighted association for the concepts in the user's profile is established for every new experience of the user. The user's activity combined with the user's profile are used together to filter and recommend relevant content.
@cite_16
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Insights into the behavior and preference of mobile device users from their web browsing application activities are critical components of any successful dynamic content recommendation system, mobile advertisement platform, or web personalization initiative. In this paper we use an unsupervised topic model to understand the interests of the cellular users based upon their browsing profile. We posit that the length of time a user remains on a given website is positively correlated with the user’s interest in the website’s content. We propose an extended model to integrate this duration information efficiently by oversampling the URLs.