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.
|Title of host publication||RecSys 2018 - 12th ACM Conference on Recommender Systems|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||4|
|State||Published - 27 Sep 2018|
|Event||12th ACM Conference on Recommender Systems, RecSys 2018 - Vancouver, Canada|
Duration: 2 Oct 2018 → 7 Oct 2018
|Name||RecSys 2018 - 12th ACM Conference on Recommender Systems|
|Conference||12th ACM Conference on Recommender Systems, RecSys 2018|
|Period||2/10/18 → 7/10/18|
Bibliographical notePublisher Copyright:
© 2018 Association for Computing Machinery.
- Collaborative Filtering
- Recommender Systems