Information access systems mediate how we find and discover information in nearly every walk of life. The ranking models powering these systems base their predictions on users' historical interactions to cater to the wide variety of users and workflows that leverage them. However, during a task session, personalized predictions often fall short of user's expectations, with users desiring greater control over a system. Greater control, in turn, leads to greater user trust and satisfaction in using a system. In this thesis, I explore methods to dynamically update personalized rankings through user interaction with ranking models. I explore control in various retrieval tasks through 1) expressive natural language queries, 2) control over latent user representations, and 3) control over both queries and latent user representations.
First, I explore long-form narrative queries as a way for users to express rich context-dependent preferences in a narrative-driven recommendation (NDR) task. Here, I propose MINT - a data augmentation strategy leveraging LLMs to generate long-form narrative queries from historical user interactions to allow the training of effective NDR models. Next, I propose LACE, a text recommendation model that represents users with a transparent concept-based user profile inferred from historical user documents. The concepts function as an interpretable bottleneck within a neural recommender, allowing users to control the underlying model. To allow control through queries and latent user representations, I introduce CtrlCE, a controllably personalized crossencoder that leverages the concept-value profiles introduced in LACE. Specifically, I treat concept-value profiles as editable memories of a user's historical documents and augment a transformer crossencoder with these memories. Allowing crossencoders to condition on large amounts of user data while allowing users effective control over personalization. Further, in augmenting crossencoders with editable memories I train a calibrated mixing model to combine non-personalized query-document scores with personalized user-document scores and only solicit user input when necessary. Finally, having leveraged concept-value memories as a user representation for controllable personalization, I explore such a corpus representation as an interactive topic model, introducing EdTM. I show EdTM to support a variety of user interactions, scale to large corpora, and effectively leverage expressive LLM scoring functions opening the possibility of a wider variety of user control mechanisms over personalized information access tasks in future work.
Advisors: Andrew McCallum and Hamed Zamani