Using community trained recommender models for enhanced information retrieval

Research in Information Retrieval (IR) seeks to develop methods which better assist users in finding information which is relevant to their current information needs. Personalization is a significant focus of research for the development of next generation of IR systems. Commercial search engines are exploring methods to incorporate models of the user’s interests to facilitate personalization in IR to improve retrieval effectiveness. However, in some situations there may be no opportunity to learn about the interests of a specific user on a certain topic. This is a significant challenge for IR researchers attempting to improve search effectiveness by exploiting user search behaviour. We propose a solution to this problem based on recommender systems (RSs) in a novel IR model which combines a recommender model with traditional IR methods to improve retrieval results for search tasks, where the IR system has no opportunity to acquire prior information about the user’s knowledge of a domain for which they have not previously entered a query. We use search behaviour data from other previous users to build topic category models based on topic interests. When a user enters a query on a topic which is new to this user, but related to a topical search category, the appropriate topic category model is selected and used to predict a ranking which this user may find interesting based on previous search behaviour. The recommender outputs are used in combination with the output of a standard IR system to produce the overall output to the user. In this thesis, the IR and recommender components of this integrated model are investigated