Learning Deep Matching-Aware Network for Text Recommendation using Clickthrough Data
Haonan Liu, Nankai Lin, Zitao Chen, Ke Li, Shengyi Jiang · 2019
With the trend of information globalization, the volume of text information is exploding, which results in the information overload problem. Text recommendation system has shown to be a valuable tool to help users in such situations of information overload. In general, most researchers define text recommendation as a static problem, ignoring sequential information. In this paper, we propose a text recommendation framework with matching-aware interest extractor and dynamic interest extractor. We apply the Attention-based Long Short-Term Memory Network (LSTM) to model a user’ s dynamic interest. Besides, we model a user’ s static interest with the idea of semantic matching. We integrate dynamic interest and static interest of users’ and decide whether to recommend a text. We also propose a reasonable method to construct a text recommendation dataset with clickthrough data from CCIR 2018 shared task Personal Recommendation. We test our model and other baseline models on the dataset. The experiment shows our model outperforms all the baseline models and a state-of-the-art model, and the Fl-score of our model reaches 0.76.