標題: NEWS RECOMMENDATION BASED ON COLLABORATIVE SEMANTIC TOPIC MODELS AND RECOMMENDATION ADJUSTMENT
作者: Liao, Yu-Shan
Lu, Jun-Yi
Liu, Duen-Ren
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: Recommendation;Latent topic analysis;Collaborative topic model;Recommendation adjustment
公開日期: 1-Jan-2019
摘要: Providing news recommendations is an important trend for online news websites to attract more users and create more benefits. In this research, we propose a novel recommendation approach for recommending news articles. We propose A Collaborative Semantic Topic Model and an ensemble model to predict user preferences based on combining Matrix Factorization with articles' semantic latent topics derived from word embedding and Latent Dirichlet Allocation. The proposed ensemble model is further integrated with a recommendation adjustment mechanism to adjust users' online recommendation lists. We evaluate the proposed approach via offline experiments and online evaluation on a real news website. The experimental result demonstrates that our proposed approach can improve the recommendation quality of recommending news articles.
URI: http://hdl.handle.net/11536/154277
ISBN: 978-1-7281-2816-0
ISSN: 2160-133X
期刊: PROCEEDINGS OF 2019 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC)
起始頁: 54
結束頁: 59
Appears in Collections:Conferences Paper