Heterogeneity Involved Network-based Algorithm Leads to Accurate and Personalized Recommendations

TL;DR

This paper introduces a heterogeneous heat conduction algorithm (HHC) for network-based recommendations that improves accuracy and personalization by considering object heterogeneity and source object degree, especially aiding cold start objects.

Contribution

The paper proposes the HHC algorithm that incorporates source object degree into heat conduction, enhancing recommendation quality without extra parameters or information.

Findings

HHC outperforms BHC and HHM in accuracy and personalization.

HHC effectively alleviates cold start recommendation issues.

HHC shows superior performance on real datasets like Netflix, RYM, and MovieLens.

Abstract

Heterogeneity of both the source and target objects is taken into account in a network-based algorithm for the directional resource transformation between objects. Based on a biased heat conduction recommendation method (BHC) which considers the heterogeneity of the target object, we propose a heterogeneous heat conduction algorithm (HHC), by further taking the source object degree as the weight of diffusion. Tested on three real datasets, the Netflix, RYM and MovieLens, the HHC algorithm is found to present a better recommendation in both the accuracy and personalization than two excellent algorithms, i.e., the original BHC and a hybrid algorithm of heat conduction and mass diffusion (HHM), while not requiring any other accessorial information or parameter. Moreover, the HHC even elevates the recommendation accuracy on cold objects, referring to the so-called cold start problem, for effectively relieving the recommendation bias on objects with different level of popularity.