Task-Based User Modelling for Personalization via Probabilistic Matrix Factorization

We introduce a novel approach to user modelling for behavioral targeting: task-based user representation and present an approach based on search task extraction from search logs wherein users are represented by their actions over a task-space. Given a web search log, we extract search tasks performed by users and nd user representations based on these tasks. More specically, we construct a user-task association matrix and borrow insights from Collaborative Filtering to learn low-dimensional factor model wherein the interests/preferences of a user are determined by a small number of latent factors. We compare the performance of the proposed approach on the task of collaborative query recommendation on publicly available AOL search log with a standard term-similarity baseline and discuss potential future research directions.