learning techniques in social and location-based service recommendation
As the mobile technologies advance, location-based services (LBS) become promising for improving people's daily lives. These services not only can identify users' activities at a location, but also can attain users' social activity networks. The context-aware information including location and social information of mobile users can help to analyze their common interests and current commercial intentions. This is especially helpful in determining the marketing strategy in a special region on behalf of the users. Upon the rapid development and deployment of mobile location-based services, various kinds of location-based and social data will be enormous. How to learn uses' interests and commercial intentions from the ubiquitously available data come to be essential for the innovation of location-based services. In this talk, we will explore related learning techniques to tackle the data mining problems associated with social and location-based service recommendation. Our talk includes the following topics: What kinds of learning techniques can be adopted and developed to solve the location-based and social data learning problems? What are the pros and cons for content-based learning techniques and collaborative filtering techniques? How to include location-based or social information into content-based learning techniques for effective data mining? How to incorporate location-based or social information into collaborative filtering techniques for effective data mining? How to perform recommendation based on the learning results? What are the future research issues and promising applications in this emerging area?
recommendation location based service