A Diversified Recommendation Method for UCL in Broadcast-Storage Network

By introducing broadcast distribution into TCP/IP, Broadcast-Storage network has clear advantages in reducing the redundant traffic in the Internet and remitting information overload problem. Uniform content label (UCL) is used to express the needs of users and help users obtain the information resources in Broadcast-Storage network. In the process of UCL recommendation, one key problem that needs to be solved is that how to improve the diversity of recommendation based on the features of Broadcast-Storage network, e.g., rich semantic information and high novelty. To solve this problem, this paper proposes a diversification method UDSCT for UCL recommendation based on semantic cover tree. UDSCT consists of two components. The first one is constructing the semantic cover tree for UCLs, which obeys some proposed invariants and considers the semantic information of UCL and the ratings from users. Besides that, new UCLs are given priority to improve the novelty of the whole UCL list. The second component is the query of diversified UCL list, which uses simple tree query and heuristic list supplement operation to obtain the diversified UCL list fast and returns specified UCL sets rapidly according to users’ need. Theoretical analysis and a series of experiments results show that, UDSCT outperforms some benchmark algorithms and is suitable for Broadcast-Storage network.