Personalised content recommendation based on field authorities in transparent computing. (2016) - Li, Shuang; Hu, Wei; Zhang, Yaoxue; Zhou, Yuezhi; Xue, Zhi
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.