Guilherme Bittencourt
ABSTRACT
Current advances in Deep Neural Networks have motivated recent studies to return their interest in Review-Aware Recommender Systems (RSs). In this sense, we employ a systematic mapping approach by selecting 56 papers published on the main vehicles of the area, such as RecSys, VLDB, SIGIR, and others. Reading these works and synthesising their achievements, we provide an updated picture of this field by highlighting relevant outcomes, contributions, and limitations. Especially, we have identified two huge limitations in the area. First, many of these works do not provide their code sources. Second, there is a prevalent tendency to prioritize accuracy above other quality dimensions, despite the consensus within the community to assess the practical effectiveness of RSs through many metrics. Both observations create constraints that restrict reproducibility and hinder straightforward comparisons in the field. Addressing this gap, this work also provides an open-source library that compiles the principal strategies proposed in the literature. Furthermore, we conducted a comprehensive evaluation through several datasets and metrics. Our findings indicate that while no single algorithm demonstrates absolute superiority, RSs based on neural networks have particularly exhibited the most competitive performance.
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Index Terms
- A Survey on Review - Aware Recommendation Systems
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