A collaborative filtering algorithm based on memory

Collaborative filtering is one of the most successful technologies for building recommender system. However, existing collaborative filtering algorithms can not promptly reflect the change of users´ interest. For this reason, this paper introduces the human brain´s characteristics of memory and forgetting to personalized recommendation, and proposes a collaborative filtering algorithm based on memory. On the one hand, the effective use of short-term memory reflects users´ recent interest; On the other hand, long-term memory emphasizes the importance of users´ early interest; at the same time, it combines the short-term memory with the long-term memory and proposes the reconciled memory, which makes the recommender system adaptively track the change of users´ interest. The experimental results show that the improved algorithm has higher accuracy than existing algorithms.