ausblenden:
Schlagwörter:
Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL,Computer Science, Information Retrieval, cs.IR,cs.SI,Statistics, Machine Learning, stat.ML
Zusammenfassung:
Online reviews provide viewpoints on the strengths and shortcomings of
products/services, influencing potential customers' purchasing decisions.
However, the proliferation of non-credible reviews -- either fake (promoting/
demoting an item), incompetent (involving irrelevant aspects), or biased --
entails the problem of identifying credible reviews. Prior works involve
classifiers harnessing rich information about items/users -- which might not be
readily available in several domains -- that provide only limited
interpretability as to why a review is deemed non-credible. This paper presents
a novel approach to address the above issues. We utilize latent topic models
leveraging review texts, item ratings, and timestamps to derive consistency
features without relying on item/user histories, unavailable for "long-tail"
items/users. We develop models, for computing review credibility scores to
provide interpretable evidence for non-credible reviews, that are also
transferable to other domains -- addressing the scarcity of labeled data.
Experiments on real-world datasets demonstrate improvements over
state-of-the-art baselines.
