Cache-Decision Policy using User Tastes

In recent years, the majority of internet traffic is video traffic such as YouTube and NetFlix, and reducing video traffic is an essential subject for network providers. Placing copy of original video contents in replication servers close to users has a chance to reduce the traffic in the networks. However, the storage capacity of a replication server is generally limited, so it is necessary to select video contents which will be requested in the near future. In the existing researches, temporarily or regionally popular contents are selected by focusing on the past popularity. In this paper, we would like to focus on another aspect of content request characteristics, user taste, and we propose to place contents which are not so popular but expected to be viewed by other users having similar tastes in the near future. Users with similar taste tend to request similar video contents, so we propose to select contents based on the measured tastes of users to place in the storage of replication servers. In the proposed method, cache storage is divided into two parts, a large static part and a small dynamic part. Cache-decision policy using user tastes is applied for the static part. In the dynamic part, LRU (Least Recently Used) cache-decision policy is applied to store contents having high popularity just after they are generated. The numerical results using MovieLens dataset show that the proposed method can make user groups with similar taste and improve the cache hit ratio compared with the LRU.