Topics, Tasks & Beyond: Learning Representations for Personalization

Abstract

Accurate understanding of a user's interests, preferences and behaviours is possibly one of the most critical research challenges faced while developing personalized systems for behavior targeting and information access. We intend to develop comprehensive latent variable models for web search personalization which jointly models user's topical interests along with user's click based relevance preferences while at the same time taking into account user's intended search tasks along with information about other similar users. We further augment this model by incorporating topic-level relevance parameters, which, to the best of our knowledge, is the first attempt at modeling result ranking preferences at the topic level. Additionally, we intend to explore the possibility of modeling users in terms of the search tasks they perform thereby coupling users' topical interests with their search task behavior to learn user representations. Finally, we wish to evaluate the proposition of extending user representations to hierarchical structures as an alternative to existing flat representations. The evaluation of these alternative approaches for user modeling is based on their performance on a variety of tasks such as collaborative query recommendations, user cohort modeling and search result personalization. This proposal provides the motivation to pursue these research directions, summarizes key research problems being targeted, glances through potential ways of tackling these research challenges and highlights some initial results obtained.