Volume 29, Issue 2
DOI: 10.24205/03276716.2020.255
PREDICTION OF SPORTS NEWS CLICK-THROUGH RATE BASED ON NEURAL NETWORKS
Abstract
In the era of new media, major search engines and news portals are concerned with the accurate prediction of the click-through rate (CTR). Against the backdrop, this paper improved the recurrent neural network (RNN) for better prediction of the CTR of sports news. First, the curve neurons in the RNN were replaced with the long short-term memory (LSTM) structure, such that the improved RNN can record longterm historical data. Besides, the steep gradient descent (SGD) algorithm and cross entropy were introduced to further optimize the computing method and objective function of the network. Furthermore, the accumulated calculation output mode was adopted to solve the vanishing gradient problem. Finally, the improved RNN was compared with existing methods like the backpropagation neural network (BPNN) and logistic regression (LR) algorithm through a contrastive experiment. The results show that our improved RNN achieved the minimum log-likelihood loss (logloss), i.e. the most accurate CTR prediction for sports news. The research results shed new light on the design of effective news delivery strategy for news websites.
Keywords
Sports News, Click-Through Rate (CTR), Prediction, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM).