A Novel Collaborative Filtering Recommendation Approach Based on Field Authorities

Abstract:

This paper presents a novel collaborative filtering recommendation algorithm based on field authorities which simulates the real life word of mouth recommendation mode. It uses the specialistic knowledge from field authorities of different genres, and successfully addresses data sparsity and noise problems existing in traditional collaborative filtering. Meanwhile it also improves prediction accuracy and saves computational overhead effectively. Experiments on MovieLens datasets show that the accuracy of our algorithm is significantly higher than collaborative filtering approach based on experts, and has larger scope because of no external data limitations. Meanwhile, compared to traditional k-NN collaborative filtering, our algorithm has a better performance both in MAE and precision experiments, and the computational overhead has a decrease of 19.2% while they provide the same accuracy level.

• Periodical: Advanced Materials Research (Volumes 765-767)
• Pages: 989-993
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.765-767.989
• Online since: September 2013
• Authors: Zhi Xue, Yao Xue Zhang, Yue Zhi Zhou, Wei Hu
• Keywords: Collaborative Filtering (CF), Field Authority, Rating Prediction, Recommender System

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