Controllable Recommenders using Deep Generative Models and Disentanglement
Samarth Bhargav,
Evangelos Kanoulas
Abstract:In this paper, we consider controllability as a means to satisfy dynamic preferences of users, enabling them to control recommendations such that their current preference is met. While deep models have shown improved performance for collaborative filtering, they are generally not amenable to fine grained control by a user, leading to the development of methods like deep language critiquing. We propose an alternate view, where instead of keyphrase based critiques, a user is provided 'knobs' in a disentangled la… Show more
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