LAUSR

Edge caching enables low-delay and high-quality data services for the Industrial Internet. However, traditional popularity-based edge caching ignores the diversity and evolution of user interest, especially among user groups, and therefore has the limited quality of experience guarantees for users. In this regard, a recommendation-aided edge caching approach is proposed to leverage the time-varying user interest. Specifically, a dynamic interest capture model was proposed to mine the individual user interest, based on which, a group interest aggregation algorithm is then studied to determine the content caching strategies for edge nodes. Thereafter, an edge content recommendation is further proposed to optimize the cache hit ratio while ensuring a satisfying recommendation hit ratio based on the personalized user interest and given caching decision. The effectiveness of the proposed approach is finally validated by comparing it with other baseline approaches.