Table of Contents
Information, Volume 10, Issue 5 (May 2019)
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Cover Story (view full-size image) Recommender systems have gained a lot of popularity due to their widespread adoption in various [...] Read more. Recommender systems have gained a lot of popularity due to their widespread adoption in various industries, such as entertainment and tourism. Numerous research efforts have focused on formulating and advancing state-of-the-art systems that recommend the right set of items to the right person. However, these recommender systems are hard to compare, because the published evaluation results are computed on diverse datasets and obtained using different methodologies. In this paper, we researched and prototyped an offline evaluation framework called Sequeval that is designed to evaluate recommender systems capable of suggesting sequences of items. Sequeval is publicly available, and it aims to become a focal point for researchers and practitioners when experimenting with sequence-based recommender systems, providing comparable and objective evaluation results. View this paper