Multi-task learning for boosting with application to web search ranking

By Olivier Chapelle, Pannagadatta K. Shivaswamy, Srinivas Vadrevu, Kilian Q. Weinberger, Ya Zhang, Belle L. Tseng. 2010

111 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hamed Zamani
2. W. Bruce Croft
3. Jiafeng Guo
4. Qiang Yang
5. Qingyao Ai
6. Jamie Callan
7. Jimmy J. Lin
8. Xueqi Cheng

In Information Systems

1. Hamed Zamani
2. W. Bruce Croft
3. Qingyao Ai
4. Jiafeng Guo
5. Yixing Fan
6. Liu Yang
7. Xueqi Cheng
8. Liang Pang

Citations in the corpus

• A Survey on Multi-Task Learning (2017, neutral): “In [203], a multi-task boosting, where each task has a common classifier and a task-specific classifier in a way similar to [73], is proposed for web search ranking.”
• Distributed Multi-Task Learning (2015, methodology): “It has been successfully exploited in, e.g., spam filtering [Weinberger et al., 2009], web search [Chapelle et al., 2010], disease prediction [Zhou et al., 2013] and eQTL mapping [Kim and Xing, 2010].”
• A Deep Look into Neural Ranking Models for Information Retrieval (2019, background): “To do so, previous studies mostly focus on constructing regularizations or restrictions on model optimizations so that the final model is not specifically designed for a single ranking objective [104, 105].”
• Learning to Rank for Information Retrieval and Natural Language Processing (2011, unknown): “[17] propose a boosting algorithm to multi-task ranking.”
• Co-Training for Domain Adaptation (2011, methodology): “The final set of domain adaptation algorithms, which we compared against but did not describe, are those which actively seek to minimize the labeling divergence between domains using multi-task techniques [1, 8, 9, 12, 21, 27].”
• Feature-based transfer learning with real-world applications (2010, background): “Web applications of transfer learning include text classification across domains [151, 47, 48, 72, 195, 207, 36, 204, 213, 203], advertising [40, 39], learn to rank across domains [10, 192, 35, 68] and multi-domain collaborative filtering [103, 102, 29, 223, 143].”
• Sparse Bayesian Multi-Task Learning (2011, background): “More recently, the abundant literature on multi-task learning demonstrated that performance indeed improves when the tasks are related [6, 31, 2, 14, 13].”
• Multitask Diffusion Adaptation Over Networks (2013, background): “Themultitask learning problem is relevant in several machine learning formulations and has found applications in web page categorization [34], web-search ranking [35], and disease progression modeling [36], among other areas.”
• Transitive Transfer Learning (2015, background): “In practice, for example, classi-fiers for different tasks can be designed to share some global parameters [11] or even a global classifier [8].”