Personalizing Information Retrieval Using Task Features, Topic Knowledge, and Task Products

Personalization of information retrieval tailors search towards individual users to meet their particular information needs. Personalization systems obtain additional information about users and their contexts beyond the queries they submit to the systems, and use this information to bring the desired documents to top ranks. Such additional information can come from many sources, and this study looks specifically at the features of users work tasks, users’ degrees of familiarity with work task topics, and task products that users generate in accomplishing their tasks. The study will explore whether or not taking account of task features and topic familiarity help predict a document’s usefulness from its display time. The study will also investigate whether or not expanding queries by significant terms extracted from task product(s) and used/saved pages improves search performance. To these ends, a controlled lab experiment will be conducted to gather related data. Twenty-four participants will be recruited, each coming three times within a two-week period to work on three sub-tasks in a general work task. Data will be collected by two major means: logging software that records user-system interactions, and questionnaires that elicit users’ background information and their perceptions on a number of aspects. Data will be analyzed using various statistical analysis methods. The results are hoped to provide significant evidence on personalizing search by taking account of contextual factors and thus to extend the literature in personalization of information retrieval.