text: 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.
  @cite_15: This paper describes a novel approach for gathering profile information about mobile phone users. The focus is on information that can be used to enhance targeting of advertisements. (The ads might be delivered into the mobile phones, or to other devices such as the user's IPTV.) Unlike previous approaches, we use a two-tiered approach for learning end-user habits and preferences. In this approach the first tier involves statistical learning from network observable data (in the current paper, primarily logs of cell towers visited), and the second tier involves explicit queries to the user (in the current paper, to ask, e.g., what kinds of activities the user does in a given region that he frequents). The user might be willing to answer occasional queries of this sort through offers of service discounts, or to be able to receive more relevant ads. The paper focuses on two key aspects of our approach, which correspond to how the two tiers are instantiated in the current version of the prototype system that we have developed at Bell Labs. The first concerns the statistical techniques used to determine information about regions visited, along with the frequency of visits, typical durations, and typical visit times. These techniques were developed based on a training set consisting of logs of 6 users with mobile devices over a period of several months. The techniques address issues that arise when a given small region is serviced by multiple cell towers (in which case oscillations between cell towers can be confused with movement between regions). The second key aspect concerns optimizing the order in which queries are presented to users, in a context where different query answers have different value for the advertising process. (The values of answers might be influenced by the mix of advertising campaigns from which ads are to be matched against users.) Optimization is NP-complete in a relatively general context. We develop a polynomial time algorithm which yields optimal sequences for the case where the family of queries to be asked satisfies a tree-based property. This is extended to create a heuristic polynomial time algorithm for the general case.
  @cite_16: 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.
summary: 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.