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
2056102005
Social Curation Service (SCS) is a new type of emerging social media platform, where users can select, organize and keep track of multimedia contents they like. In this paper, we take advantage of this great opportunity and target at the very starting point in social media: user profiling, which supports fundamental applications such as personalized search and recommendation. As compared to other profiling methods in conventional Social Network Services (SNS), our work benefits from the two distinguishable characteristics of SCS: a) organized multimedia user-generated contents, and b) content-centric social network. Based on these two characteristics, we are able to deploy the state-of-the-art multimedia analysis techniques to establish content-based user profiles by extracting user preferences and their social relations. First, we automatically construct a content-based user preference ontology and learn the ontological models to generate comprehensive user profiles. In particular, we propose a new deep learning strategy called multi-task convolutional neural network (mtCNN) to learn profile models and profile-related visual features simultaneously. Second, we propose to model the multi-level social relations offered by SCS to refine the user profiles in a low-rank recovery framework. To the best of our knowledge, our work is the first that explores how social curation can help in content-based social media technologies, taking user profiling as an example. Extensive experiments on 1,293 users and 1.5 million images collected from Pinterest in fashion domain demonstrate that recommendation methods based on the proposed user profiles are considerably more effective than other state-of-