Diffusion-like recommendation with enhanced similarity of objects

作者：An, Ya-Hui; Dong, Qiang^*; Sun, Chong-Jing; Nie, Da-Cheng; Fu, Yan

来源：Physica A: Statistical Mechanics and Its Applications , 2016, 461: 708-715.

DOI：10.1016/j.physa.2016.06.027

• Recommender systems
• Bipartite networks
• Resource-allocation similarity
• Diffusion-like algorithms

In the last decade, diversity and accuracy have been regarded as two important measures in evaluating a recommendation model. However, a clear concern is that a model focusing excessively on one measure will put the other one at risk, thus it is not easy to greatly improve diversity and accuracy simultaneously. In this paper, we propose to enhance the Resource-Allocation (RA) similarity in resource transfer equations of diffusion-like models, by giving a tunable exponent to the RA similarity, and traversing the value of this exponent to achieve the optimal recommendation results. In this way, we can increase the recommendation scores (allocated resource) of many unpopular objects. Experiments on three benchmark data sets, MovieLens, Netflix and RateYourMusic show that the modified models can yield remarkable performance improvement compared with the original ones.

• 出版日期2016-11-1
• 单位电子科技大学; 中国电子科技集团公司第三十研究所