A New User Similarity Computation Method for Collaborative Filtering Using Artificial Neural Network

A User-User Collaborative Filtering (CF) algorithm predicts the rating of a particular item for a given user based on the judgment of other users, who are similar to the given user. Hence, measuring similarity between two users turns out to be a crucial and challenging task as the similarity function is the core component of the item rating prediction function for a particular user. In this paper, we investigate the effectiveness of a multilayer feed-forward artificial neural network as a similarity measurement function. We model similarity between two users as a function that consists of a set of adaptive weights and attempt to train a neural network to optimize the weights. Specifically, our contribution lies in designing an error function for the neural network, which optimizes the network and sets weights in such a way that enables the neural network to produce a reasonable similarity value between two users as its output. Through experimentation on Movielens dataset, we conclude that neural network, as a similarity function, gains more accuracy and coverage compared to the Genetic Algorithm (GA) based similarity architecture proposed by Bobadilla et al.

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