Batch versus Sequential Active Learning for Recommender Systems

TL;DR

This paper compares batch and sequential active learning strategies in recommender systems, finding that sequential mode generally yields more accurate recommendations, especially with FunkSVD predictor on dense datasets.

Contribution

It provides a comparative analysis of five active learning algorithms combined with three predictors, highlighting the effectiveness of sequential mode in recommender systems.

Findings

Sequential mode outperforms batch mode in dense datasets.

FunkSVD predictor works best with active learning.

Differences among active learning algorithms are minimal.

Abstract

Recommender systems have been investigated for many years, with the aim of generating the most accurate recommendations possible. However, available data about new users is often insufficient, leading to inaccurate recommendations; an issue that is known as the cold-start problem. A solution can be active learning. Active learning strategies proactively select items and ask users to rate these. This way, detailed user preferences can be acquired and as a result, more accurate recommendations can be offered to the user. In this study, we compare five active learning algorithms, combined with three different predictor algorithms, which are used to estimate to what extent the user would like the item that is asked to rate. In addition, two modes are tested for selecting the items: batch mode (all items at once), and sequential mode (the items one by one). Evaluation of the recommender in terms of rating prediction, decision support, and the ranking of items, showed that sequential mode produces the most accurate recommendations for dense data sets. Differences between the active learning algorithms are small. For most active learners, the best predictor turned out to be FunkSVD in combination with sequential mode.