Henry Song

Mobile Situation-Aware Task Recommendation Application

With more and more applications available on mobile devices, it has become increasingly difficult for users to find a desired application. Although research has been conducted for situation-awarere commendations on mobile devices, none addresses this problem; most research is for media content recommendations. Moreover, existing approaches assume predefined situations and/or user-specified profiles; some require users to intentionally train their devices before using them for recommendations. We believe that what defines a situation and what applications are preferred in the situation not only vary from user to user but also change over time, and therefore these assumptions and requirements are impractical for ordinary consumers. In this paper, we will describe our approach of using unsupervised learning, specifically co-clustering, to derive latent situation-based patterns from usage logs of user interactions with the device and environments and use the patterns for task and communication mode recommendations.

Nnon-collaborative interest mining for personal devices

In our daily life we frequently use mobile devices to interact with the people and things on the Internet. However, finding the right things when needed is getting difficult and frustrating. In this paper, we introduce a relatively new problem of non-collaborative personal interest mining using contexts and ratings available for items of interest. We present multi-step algorithms to extract personal situational interests from mobile phone usage logs without depending on other peoples data. The algorithms are based on clustering or a direct analogy from collaborative filtering. We provide extensive experimental results with our accuracy measure for synthetic data sets. The main advantages of our algorithms are: 1) no need for the user to train the phone actively, 2) no need for prior knowledge of the situations contained in a data set, 3) light-weight and running completely on a personal mobile phone and 4) good performance over low data densities. We also present a SmartSearch application. Upon user request, it automatically constructs search queries based on learned user interests and obtains information and advertisements for the user that suit the users situation.

Interplay: a middleware for integration of devices, services and contents in the home networking environment

Current home networking technologies such as UPNP, Jini and HAVi, only focus on connecting devices electronically in a variety of different forms that allow simple device interactions. The result is that devices act in incompatible islands, making their use confusing to users and a nightmare for application developers. To realize a smart home environment, we need to seamlessly integrate devices and services into our every day lives. In this paper, we describe a middleware platform to provide the integration of devices, services and contents in a home. Our middleware facilitates integration of both data (content, preferences, etc.) and applications across home networking technologies to improve consumer and developer usability.

SmartSearch: Situation-Aware Web Search on Mobile Devices

To provide consumers with the right information at the time of need, we developed a SmartSearch application that is able to extract a users situational interests from usage data. It automatically constructs search queries based on situational interests. We extract the situational interests automatically without prior training and user involvement. In addition, SmartSearch is a client-side solution running completely on a mobile device to protect users privacy.

Extracting Co-locator context

Having reliable context sources is very important for context-aware applications and the devices around a user can be a useful context for many applications. While the importance of ‘devices around’ as a context has been highlighted many times, to the best of our knowledge, there is no systematic mechanism reported to identify and capture this context. In this paper we report our mechanism for extracting patterns that we call co-locators from the surrounding Bluetooth devices. The co-locator context can then be used as an indication about the users surroundings. The context can be directly associated with user behavior in the surroundings, which in turn can be used for recommendation purposes. The context can also be used as a supplemental location indicator in the absence of other location mechanisms such as GPS. We then show that our experimental results support the value of using extracted co-locator patterns as good supplemental location indicators for several applications.