Rank and rate: multi-task learning for recommender systems

Guy Hadash, Oren Sar Shalom, Rita Osadchy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 12th ACM Conference on Recommender Systems
PublisherPubl by ACM
Pages451-454
Number of pages4
ISBN (Electronic)9781450359016
DOIs
StatePublished - 2018
Event12th ACM Conference on Recommender Systems, RecSys 2018 - Vancouver, Canada
Duration: 2 Oct 20187 Oct 2018

Publication series

NameRecSys 2018 - 12th ACM Conference on Recommender Systems

Conference

Conference12th ACM Conference on Recommender Systems, RecSys 2018
Country/TerritoryCanada
CityVancouver
Period2/10/187/10/18

Bibliographical note

Publisher Copyright:
© 2018 Association for Computing Machinery.

Keywords

  • Collaborative Filtering
  • Recommender Systems

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture

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