Personalized news recommendation based on an improved conditional restricted Boltzmann machine

• Published in: Electronic library Vol. 39; no. 4; pp. 553 - 571
• Main Authors: Zhong, Linxia, Wei, Wei, Li, Shixuan
• Format: Journal Article
• Language: English
• Published: Oxford Emerald Publishing Limited 04.11.2021; Emerald Group Publishing Limited
• Subjects: Algorithms; Cold; Collaboration; Customization; Learning Processes; Literature Reviews; Networks; Neural networks; Neurons; News; Preferences; Recommender systems; Sampling; Scientific Concepts; Semantics; User behavior; Weightlifting; Recommendations; Restricted Boltzmann machine; LDA2vec; RBM; Topic models; News
• ISSN: 0264-0473; 1758-616X
• DOI: 10.1108/EL-06-2020-0165

Purpose Because of the extensive user coverage of news sites and apps, greater social and commercial value can be realized if users can access their favourite news as easily as possible. However, news has a timeliness factor; there are serious cold start and data sparsity in news recommendation, and news users are more susceptible to recent topical news. Therefore, this study aims to propose a personalized news recommendation approach based on topic model and restricted Boltzmann machine (RBM). Design/methodology/approach Firstly, the model extracts the news topic information based on the LDA2vec topic model. Then, the implicit behaviour data are analysed and converted into explicit rating data according to the rules. The highest weight is assigned to recent hot news stories. Finally, the topic information and the rating data are regarded as the conditional layer and visual layer of the conditional RBM (CRBM) model, respectively, to implement news recommendations. Findings The experimental results show that using LDA2vec-based news topic as a conditional layer in the CRBM model provides a higher prediction rating and improves the effectiveness of news recommendations. Originality/value This study proposes a personalized news recommendation approach based on an improved CRBM. Topic model is applied to news topic extraction and used as the conditional layer of the CRBM. It not only alleviates the sparseness of rating data to improve the efficient in CRBM but also considers that readers are more susceptible to popular or trending news.