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.
In @cite_16 the authors propose a mobile recommender system for people in leisure time. The system predicts the current activity (eating,reading or shopping) from the context (time, location) and the behaviour of the user. The predicted activity combined with preference models of the user, are used to filter and recommend relevant content. To provide relevant advertisements to mobile users, authors in @cite_15 build a profile of each region visited by the user. Statistical techniques are then used to extract information from the visited regions, like "the frequency of visits", "duration and time of typical visits", and the user's profile is built on the basis of questionnaires.
@cite_15 @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. This paper presents a context-aware mobile recommender system, codenamed Magitti. Magitti is unique in that it infers user activity from context and patterns of user behavior and, without its user having to issue a query, automatically generates recommendations for content matching. Extensive field studies of leisure time practices in an urban setting (Tokyo) motivated the idea, shaped the details of its design and provided data describing typical behavior patterns. The paper describes the fieldwork, user interface, system components and functionality, and an evaluation of the Magitti prototype. Most of existing e-commerce recommender systems aim to recommend the right product to a user, based on whether the user is likely to purchase or like a product. On the other hand, the effectiveness of recommendations also depends on the time of the recommendation. Let us take a user who just purchased a laptop as an example. She may purchase a replacement battery in 2 years (assuming that the laptop's original battery often fails to work around that time) and purchase a new laptop in another 2 years. In this case, it is not a good idea to recommend a new laptop or a replacement battery right after the user purchased the new laptop. It could hurt the user's satisfaction of the recommender system if she receives a potentially right product recommendation at the wrong time. We argue that a system should not only recommend the most relevant item, but also recommend at the right time. This paper studies the new problem: how to recommend the right product at the right time? We adapt the proportional hazards modeling approach in survival analysis to the recommendation research field and propose a new opportunity model to explicitly incorporate time in an e-commerce recommender system. The new model estimates the joint probability of a user making a follow-up purchase of a particular product at a particular time. This joint purchase probability can be leveraged by recommender systems in various scenarios, including the zero-query pull-based recommendation scenario (e.g. recommendation on an e-commerce web site) and a proactive push-based promotion scenario (e.g. email or text message based marketing). We evaluate the opportunity modeling approach with multiple metrics. Experimental results on a data collected by a real-world e-commerce website(shop.com) show that it can predict a user's follow-up purchase behavior at a particular time with descent accuracy. In addition, the opportunity model significantly improves the conversion rate in pull-based systems and the user satisfaction utility in push-based systems. Recommendation Systems have become an important research area in mobile computing. Although various recommendation systems have been developed to help users to deal with information overload, few systems focus on proactive information recommendation. This paper presents a news recommender system that proactively pushes just-in-time personalized news articles to mobile users based on user’s contextual information as well as news content. User’s information needs are estimated based on Bayesian network technique. An Analytic Hierarchy Process (AHP) Model, which supports both Content-based filtering and Collaborative filtering, is developed to rate the relevance of news articles. The weight of contexts (criteria) is automatically adjusted via individual-based and or group-based (group decision making) assignment. The experiments show that the system can push relevant news to mobile users.