Rank and Rate: Multi-task Learning for Recommender Systems
This work addresses the need for more accurate recommendations in systems like e-commerce or streaming services, though it appears incremental as it builds on existing multi-task approaches.
The paper tackled the problem of simultaneously optimizing ranking and rating prediction tasks in recommender systems by proposing a novel multi-task framework that models a user's two-phase decision process, showing superiority over state-of-the-art methods on two benchmark datasets.
The two main tasks in the Recommender Systems domain are the ranking and rating prediction tasks. The rating prediction task aims at predicting to what extent a user would like any given item, which would enable to recommend the items with the highest predicted scores. The ranking task on the other hand directly aims at recommending the most valuable items for the user. Several previous approaches proposed learning user and item representations to optimize both tasks simultaneously in a multi-task framework. In this work we propose a novel multi-task framework that exploits the fact that a user does a two-phase decision process - first decides to interact with an item (ranking task) and only afterward to rate it (rating prediction task). We evaluated our framework on two benchmark datasets, on two different configurations and showed its superiority over state-of-the-art methods.