Behavioral Task Modeling for Entity Recommendation

Our everyday tasks involve interactions with a wide range of information. The information that we manage is often associated with a task context. However, current computer systems do not organize information in this way, do not help the user find information in task context, but require explicit user actions such as searching and information seeking. We explore the use of task context to guide the delivery of information to the user proactively, that is, to have the right information easily available at the right time. In this thesis, we used two types of novel contextual information: 24/7 behavioral recordings and spoken conversations for task modeling. The task context is created by monitoring the user's information behavior from temporal, social, and topical aspects; that can be contextualized by several entities such as applications, documents, people, time, and various keywords determining the task. By tracking the association amongst the entities, we can infer the user's task context, predict future information access, and proactively retrieve relevant information for the task at hand. The approach is validated with a series of field studies, in which altogether 47 participants voluntarily installed a screen monitoring system on their laptops 24/7 to collect available digital activities, and their spoken conversations were recorded. Different aspects of the data were considered to train the models. In the evaluation, we treated information sourced from several applications, spoken conversations, and various aspects of the data as different kinds of influence on the prediction performance. The combined influences of multiple data sources and aspects were also considered in the models. Our findings revealed that task information could be found in a variety of applications and spoken conversations. In addition, we found that task context models that consider behavioral information captured from the computer screen and spoken conversations could yield a promising improvement in recommendation quality compared to the conventional modeling approach that considered only pre-determined interaction logs, such as query logs or Web browsing history. We also showed how a task context model could support the users' work performance, reducing their effort in searching by ranking and suggesting relevant information. Our results and findings have direct implications for information personalization and recommendation systems that leverage contextual information to predict and proactively present personalized information to the user to improve the interaction experience with the computer systems.