ES_Use: An Efficient Rating Prediction Method
Rong Yang, Bing Li · IEEE Access · 2021
In an online shop scenario, learning high-quality product embedding that captures various aspects of the product is important to improve the accuracy of user rating prediction. There is a lot of research about product embedding learning, for example, the side information which is the fusion of user feedback and the appearance of a product. However, because of the diversity of a product’s aspects, taking into account only its appearance as side information is not sufficient to accurately learn its embedding. In this paper, we present a matrix co-factorization method that employs information hidden in the so-called “also-viewed” or “also-bought” products, i.e., a list of products that have also been viewed or have also been bought by a user who has viewed a target product. To improve the accuracy of the rating prediction, our first step is to find out similar users. However, suppose the dataset is very large, e.g., if we have to deal with tens of millions of users’ data, the similarity calculation among users will be very time-consuming. For dealing with this problem, we use a compact binary sketch (i.e. ES, Even Sketch for user similarity estimation) to estimate user similarity. Our experiments demonstrate the superiority of our method in comparison with a state-of-the-art baseline in generating high-accuracy rating prediction.