Probabilistic matrix factorization (PMF) and other popular approaches to collaborative filtering assume that the ratings given by users for products are genuine, and hence they give equal importance to all available ratings. However, this is not always true due to several reasons including the presence of opinion spam in product reviews. In this paper, the possibility of performing collaborative filtering while attaching weights or quality scores to the ratings is explored. The quality scores, which are determined from the corresponding review data are used to ``up--weight'' or ``down--weight'' the importance given to the individual rating while performing collaborative filtering, thereby improving the accuracy of the predictions. First, the measure used to capture the quality of the ratings is described. Different approaches for estimating the quality score based on the available review information are examined. Subsequently, a mathematical formulation to incorporate quality scores as weights for the ratings in the basic PMF framework is derived. Experimental evaluation on two product categories of a benchmark data set from Amazon.com demonstrates the efficacy of our approach.

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In Sixth ACM Conference on Recommender Systems (RecSys 2012), pp. 123--130, September 2012.

@inproceedings{raghavan.recsys12, title={Review Quality Aware Collaborative Filtering}, author={Sindhu Raghavan and Suriya Ganasekar and Joydeep Ghosh}, booktitle={Sixth ACM Conference on Recommender Systems (RecSys 2012)}, month={September}, pages={123--130}, url="http://www.cs.utexas.edu/users/ai-lab?raghavan:recsys12", year={2012} }