COLTR: Semi-supervised Learning to Rank with Co-training and Over-parameterization for Web Search

• Published in: IEEE transactions on knowledge and data engineering Vol. 35; no. 12; pp. 1 - 14
• Main Authors: Li, Yuchen, Xiong, Haoyi, Wang, Qingzhong, Kong, Linghe, Liu, Hao, Li, Haifang, Bian, Jiang, Wang, Shuaiqiang, Chen, Guihai, Dou, Dejing, Yin, Dawei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Data models; Feature extraction; Labels; Learning to Rank; Machine learning; Over-parameterization; Parameterization; Performance enhancement; Predictive models; Queries; Ranking; Search engines; Searching; Semi-supervised Learning; Semisupervised learning; Task analysis; Training; Web and internet services

While learning to rank (LTR) has been widely used in web search to prioritize most relevant webpages among the retrieved contents subject to the input queries, the traditional LTR models fail to deliver decent performance due to two main reasons: 1) the lack of well-annotated query-webpage pairs wit...