Implicit Feedback Recommendation via Implicit-to-Explicit Ordinal Logistic Regression Mapping

Abstract

One common dichotomy faced in recommender systems is that explicit user feedback-in the form of ratings, tags, or user-provided personal information- is scarce, yet the most popular source of information in most state-of-the-art recommendation algorithms, and on the other side, implicit user feedback- such as numbers of clicks, playcounts, or web pages visited in a session- is more frequently available, but there are fewer methods well studied to provide recommendations based on this kind of information. Given the current scenario, and under a situation where just implicit user feedback is available, it would be more appropriate either to provide recommendations using the implicit data and implicit-fedback-based methods, or to map implicit user feedback to explicit feedback and then use an explicit-based algorithm? On this paper, we analyze this problem in the context of music recommendation by means of a well-known implicit feedback recommendation method described in Hu et al. [1] by comparing the use of raw playcounts with the use of explicit data- user ratings- obtained by mapping implicit to explicit feedback with a novel mixedeffects logistic regression model. 1.