In this paper, we propose a spreading activation approach for collaborative
filtering (SA-CF). By using the opinion spreading process, the similarity
between any users can be obtained. The algorithm has remarkably higher accuracy
than the standard collaborative filtering (CF) using Pearson correlation.
Furthermore, we introduce a free parameter $\beta$ to regulate the
contributions of objects to user-user correlations. The numerical results
indicate that decreasing the influence of popular objects can further improve
the algorithmic accuracy and personality. We argue that a better algorithm
should simultaneously require less computation and generate higher accuracy.
Accordingly, we further propose an algorithm involving only the top-$N$ similar
neighbors for each target user, which has both less computational complexity
and higher algorithmic accuracy.