Source-linked AI summary
Information filtering via biased heat conduction
Jian-Guo Liu, Tao Zhou, Qiang Guo
TL;DR
Standard heat conduction gives diverse recommendations but performs poorly in accuracy. Biased heat conduction adjusts object-degree effects and produces accurate, diverse recommendations, with improved ranking score and diversity on MovieLens.
Problem
Standard heat conduction provides highly diverse recommendations but has poor accuracy, motivating a way to improve accuracy without sacrificing diversity.
Source: p. 2Method
Biased heat conduction adjusts degree effects in the final heat-conduction step, giving large-degree objects higher temperatures than standard HC.
Source: pp. 2, 4Results
On MovieLens, average ranking score falls from 0.1516 to 0.0852 (43.5% improvement) versus standard HC, while Hamming distance rises from 0.8820 to 0.9248.
Source: p. 3Takeaways & Limitations
BHC can recommend both objects matching users’ special tastes and common-interest objects.
Source: p. 4
Abstract
from arXiv · showhide
Heat conduction process has recently found its application in personalized recommendation [T. Zhou \emph{et al.}, PNAS 107, 4511 (2010)], which is of high diversity but low accuracy. By decreasing the temperatures of small-degree objects, we present an improved algorithm, called biased heat conduction (BHC), which could simultaneously enhance the accuracy and diversity. Extensive experimental analyses demonstrate that the accuracy on MovieLens, Netflix and Delicious datasets could be improved by 43.5%, 55.4% and 19.2% compared with the standard heat conduction algorithm, and the diversity is also increased or approximately unchanged. Further statistical analyses suggest that the present algorithm could simultaneously identify users' mainstream and special tastes, resulting in better performance than the standard heat conduction algorithm. This work provides a creditable way for highly efficient information filtering.