Da-Cheng Nie

Ya-Hui An, Qiang Dong, Chong-Jing Sun et al.

In last decades, 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 the 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.

Da-Cheng Nie, Ya-Hui An, Qiang Dong et al.

Recent decade has witnessed the increasing popularity of recommender systems, which help users acquire relevant commodities and services from overwhelming resources on Internet. Some simple physical diffusion processes have been used to design effective recommendation algorithms for user-object bipartite networks, typically mass diffusion (MD) and heat conduction (HC) algorithms which have different advantages respectively on accuracy and diversity. In this paper, we investigate the effect of weight assignment in the hybrid of MD and HC, and find that a new hybrid algorithm of MD and HC with balanced weights will achieve the optimal recommendation results, we name it balanced diffusion (BD) algorithm. Numerical experiments on three benchmark data sets, MovieLens, Netflix and RateYourMusic (RYM), show that the performance of BD algorithm outperforms the existing diffusion-based methods on the three important recommendation metrics, accuracy, diversity and novelty. Specifically, it can not only provide accurately recommendation results, but also yield higher diversity and novelty in recommendations by accurately recommending unpopular objects.