A Learning Automata-Based Singular Value Decomposition and Its Application in Recommendation System

Recommendation system nowadays plays an important role in e-commerce, by helping consumers to find their preference from tens of thousands of goods and at the same time bringing large profits to e-commerce companies. Till now many different recommendation algorithm have been proposed and achieved good effect. In the context Netflix Prize in 2006, Simon Funk proposed a matrix factorization-based recommendation algorithm named Funk-SVD, which caused a widespread concern about the use of SVD model in recommend algorithm. Traditional SVD-based recommendation algorithm employs gradient descent algorithm as its optimization strategy. In this paper, we proposed a CALA-based algorithm to perform Funk-SVD, taking into consideration that CALA, as a kind of reinforcement learning model, has a superior performance on continues parameter optimization, especially in a unknown environment. As far as we known, the whole concept of CALA-based SVD is novel and unreported in the literature. To analyze the new algorithm, we tested it on the data set of film rating and achieved an average RMSE of 0.85, which is comparable with the former algorithm.