Title: Personalised content recommendation based on field authorities in transparent computing

Authors: Shuang Li; Wei Hu; Yaoxue Zhang; Yuezhi Zhou; Zhi Xue

Addresses: Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China ' Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China ' Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China; School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, China ' Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China ' Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China

Abstract: Transparent computing (TC) provides a large number of intelligent information services. Recommendation can help users rapidly locate their desired content. Collaborative filtering (CF) is a suitable recommending technology for TC, but data sparsity and noise problems have not been effectively solved. This study proposes a novel CF approach based on field authorities to achieve the genre tendency of items by mapping tags to genres and simulates a fine-grained word-of-mouth recommendation mode. Nearest neighbours are selected from sets of experienced users as field authorities in different genres, and weights are assigned to genres according to genre tendency. The method employed in this study can efficiently solve sparsity and noise problems and have high prediction accuracy. Experiments on MovieLens datasets show that the accuracy of this approach is significantly higher than that of traditional CF and expert CF in both mean absolute error and precision.

Keywords: transparent computing; recommender systems; collaborative filtering; field authorities; rating prediction; personalisation; content recommendation; word-of-mouth recommendation; WOM; simulation; nearest neighbours; weighting; sparsity; noise.

DOI: 10.1504/IJAHUC.2016.075190

International Journal of Ad Hoc and Ubiquitous Computing, 2016 Vol.21 No.2, pp.119 - 129

Received: 22 Mar 2014
Accepted: 19 Sep 2014

Published online: 07 Mar 2016 *

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