Unifying Explicit and Implicit Feedback for Rating Prediction and Ranking Recommendation Tasks

Jadidinejad, A. H. , Macdonald, C. and Ounis, I. (2019) Unifying Explicit and Implicit Feedback for Rating Prediction and Ranking Recommendation Tasks. In: 5th ACM SIGIR International Conference on the Theory of Information Retrieval, Santa Clara, CA, USA, 02-05 Oct 2019, pp. 149-151. ISBN 9781450368810 (doi: 10.1145/3341981.3344225)

189873.pdf - Accepted Version



The two main tasks addressed by collaborative filtering approaches are rating prediction and ranking. Rating prediction models leverage explicit feedback (e.g. ratings), and aim to estimate the rating a user would assign to an unseen item. In contrast, ranking models leverage implicit feedback (e.g. clicks) in order to provide the user with a personalized ranked list of recommended items. Several previous approaches have been proposed that learn from both explicit and implicit feedback to optimize the task of ranking or rating prediction at the level of recommendation algorithm. Yet we argue that these two tasks are not completely separate, but are part of a unified process: a user first interacts with a set of items and then might decide to provide explicit feedback on a subset of items. We propose to bridge the gap between the tasks of rating prediction and ranking through the use of a novel weak supervision approach that unifies both explicit and implicit feedback datasets. The key aspects of the proposed model is that (1) it is applied at the level of data pre-processing and (2) it increases the representation of less popular items in recommendations while maintaining reasonable recommendation performance. Our experimental results - on six datasets covering different types of heterogeneous user's interactions and using a wide range of evaluation metrics - show that, our proposed approach can effectively combine explicit and implicit feedback and improve the effectiveness of the baseline explicit model on the ranking task by covering a broader range of long-tail items.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Jadidinejad, Dr Amir and Macdonald, Professor Craig and Ounis, Professor Iadh
Authors: Jadidinejad, A. H., Macdonald, C., and Ounis, I.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2019 Association for Computing Machinery
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3009820Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science