Content Recommendation Algorithm Based on Double Lists in Heterogeneous Network

Abstract

Applying recommendation algorithms in mobile edge caching can further improve the utilization of the caching and relieve the pressure of the backhaul links. The key is to capture accurate user preferences which are usually influenced by the user’s request record and current request. In this paper, we propose a content recommendation algorithm based on both history request record and current interest. The content, user preferences and user’s requests are modeled as vectors from multiple content dimensions. Based on user’s request record, we capture the user preferences vector (Pre-Vector) by using the maximum likelihood estimation. The Pre-Vector accurately reflects user preference but has hysteresis. The user current request vector (Req-Vector) can reflect the user’s current interest but its accuracy is not stable. We propose the preference-based recommendation list and the request-based recommendation list based on the Pre-Vector and the Req-Vector respectively. In order to ensure the accuracy of the recommendation list, the final recommendation list is generated based on the Pre-Vector and the Req-Vector’s cosine similarity. The simulation results show that, the proposed algorithm has improved caching hit rate compared with existing recommendation algorithms.