Diffusion-like recommendation with enhanced similarity of objects
This work addresses the challenge of balancing diversity and accuracy for users in recommendation systems, though it appears incremental as it modifies existing diffusion-like models.
The paper tackled the trade-off between diversity and accuracy in recommendation systems by enhancing Resource-Allocation similarity in diffusion-like models with a tunable exponent, achieving remarkable performance improvements on benchmark datasets like MovieLens, Netflix, and RateYourMusic.
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.