Ant Collaborative Filtering Addressing Sparsity and Temporal Effects

Abstract

Though collaborative filtering (CF) is a popular and successful recommendation technique, it still suffers from the data sparsity and users' evolving taste over time. This paper presents a new collaborative filtering scheme: the Ant Collaborative Filtering. With the mechanism of pheromone transmission between users and items, the proposed method can pinpoint most relative users and items even in the case of the sparsity situation. Also, by virtue of the evaporation of existing pheromone, the proposed method captures the evolution of user preferences over time. Experiments are performed on the standard, public datasets and two real corporate datasets, which cover both explicit and implicit rating data. The results illustrate that the proposed algorithm outperforms current approaches in terms of accuracy and changing data.