A Collaborative Filtering Recommendation Method for UCL in Broadcast-Storage Network

Problems like bandwidth congestion, content redundancy exist in the sharing of information resources. Broadcast-Storage network has a particular advantage in solving these issues because of its unique feature of one to infinite by physical broadcast. Uniform content label, UCL, is used to express the needs of users and help users understand the information resources in Broadcast-Storage environment. Due to UCL’s large quantity, how to guide users to get their preferred UCLs efficiently is quite significant. To address this problem, this paper proposes a unifying collaborative filtering method with popularity and timing (UCF-PT) for UCL recommendation. First, a pair of thresholds are set to estimate the sparsity of users and UCLs in the dataset and determine the weights of users and UCLs in recommendation. Then UCF-PT predicts the ratings of users on UCLs based on the weights and generates a recommendation list. Moreover, the method makes popular and new UCLs more likely to be recommended by considering UCL popularity and using exponential decay in recommendation. Experiments show that, compared with traditional recommendation methods, the method proposed in this paper possesses better recommendation accuracy and ensures the popularity and novelty of recommended UCLs. Therefore, it is more suitable for recommending UCLs in Broadcast-Storage environment.