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

  • Noman Bin Mannan
  • Sheikh Muhammad Sarwar
  • Najeeb Elahi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 459)

Abstract

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.

Keywords

Collaborative filtering Recommender System Similarity measures Artificial Neural Network 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Noman Bin Mannan
    • 1
  • Sheikh Muhammad Sarwar
    • 1
  • Najeeb Elahi
    • 2
  1. 1.Institute of Information TechnologyUniversity of DhakaDhakaBangladesh
  2. 2.UiT The Arctic University of NorwayNorway

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