Incremental Collaborative Filtering Considering Temporal Effects
This addresses the need for efficient, incremental updates in recommender systems for domains like entertainment, though it appears incremental as it builds on ant colony optimization for collaborative filtering.
The paper tackled the problem of making recommender systems accurate, scalable, and able to handle sparse, time-changing data by proposing Ant Collaborative Filtering, which achieved suitability for real-world, time-sensitive scenarios across movie, book, and music recommendations.
Recommender systems require their recommendation algorithms to be accurate, scalable and should handle very sparse training data which keep changing over time. Inspired by ant colony optimization, we propose a novel collaborative filtering scheme: Ant Collaborative Filtering that enjoys those favorable characteristics above mentioned. With the mechanism of pheromone transmission between users and items, our method can pinpoint most relative users and items even in face of the sparsity problem. By virtue of the evaporation of existing pheromone, we capture the evolution of user preference over time. Meanwhile, the computation complexity is comparatively small and the incremental update can be done online. We design three experiments on three typical recommender systems, namely movie recommendation, book recommendation and music recommendation, which cover both explicit and implicit rating data. The results show that the proposed algorithm is well suited for real-world recommendation scenarios which have a high throughput and are time sensitive.