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

Abstract

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.

Efficiency-Effectiveness Trade-offs in Recommendation Systems

“…This has been achieved by leveraging users' preferences [25], stated either explicitly, through ratings, or implicitly through views, clicks, etc., and by recommending items that are either (a) similar to other items (CBF) [32] or (b) items that have been consumed by similar users (CF) [29]. There are also studies [18,23,24] which combined these two approaches to improve the quality of the recommendations.…”

Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches

“…Instead of sampling (i.e., reducing) the data, some authors propose to augment the existing data instead through a pre-processing step. Such an augmen-tation could consist of incorporating certain types of item metadata or additional information about the users from external sources [29,95] 15 , or to combine implicit and explicit feedback as done, e.g., in [71]. In this latter work, considering rating data is assumed to be useful to (a) more often recommend high-quality items regardless of their (current) popularity and to (b) better leverage existing user feedback during model training.…”