Dynamically Personalizing Search Results for Mobile Users
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Vers la définition du contexte d'un utilisateur mobile de système de recherche d'information
Proceedings of the 5th French-Speaking Conference on Mobility and Ubiquity Computing
A method for mobile user profile and reasoning
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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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 involve sexplicit 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 aperiod 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 tousers, in a context where different query answers have different valuefor 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.