@article{Tsur_Rappoport_2009, title={RevRank: A Fully Unsupervised Algorithm for Selecting the Most Helpful Book Reviews}, volume={3}, url={https://ojs.aaai.org/index.php/ICWSM/article/view/13945}, DOI={10.1609/icwsm.v3i1.13945}, abstractNote={ <p> We present an algorithm for automatically ranking user-generated book reviews according to review helpfulness. Given a collection of reviews, our RevRank algorithm identifies a lexicon of dominant terms that constitutes the core of a virtual optimal review. This lexicon defines a feature vector representation. Reviews are then converted to this representation and ranked according to their distance from a "virtual core" review vector. The algorithm is fully unsupervised and thus avoids costly and error-prone manual training annotations. Our experiments show that RevRank clearly outperforms a baseline imitating the Amazon user vote review ranking system. </p> }, number={1}, journal={Proceedings of the International AAAI Conference on Web and Social Media}, author={Tsur, Oren and Rappoport, Ari}, year={2009}, month={Mar.}, pages={154-161} }