A stochastic learning-to-rank algorithm and its application to contextual advertising

A stochastic learning-to-rank algorithm and its application to contextual advertising,10.1145/1963405.1963460,Maryam Karimzadehgan,Wei Li,Ruofei Zhang

A stochastic learning-to-rank algorithm and its application to contextual advertising   (Citations: 1)
BibTex | RIS | RefWorks Download
This paper is concerned with the problem of learning a model to rank objects (Web pages, ads and etc.). We propose a framework where the ranking model is both optimized and evaluated using the same information retrieval measures such as Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP). The main difficulty in direct optimization of NDCG and MAP is that these measures depend on the rank of objects and are not differentiable. Most learning-to-rank methods that attempt to optimize NDCG or MAP approximate such measures so that they can be differentiable. In this paper, we propose a simple yet effective stochastic optimization algorithm to directly minimize any loss function, which can be defined on NDCG or MAP for the learning-to-rank problem. The algorithm employs Simulated Annealing along with Simplex method for its parameter search and finds the global optimal parameters. Experiment results using NDCG-Annealing algorithm, an instance of the proposed algorithm, on LETOR benchmark data sets show that the proposed algorithm is both effective and stable when compared to the baselines provided in LETOR 3.0. In addition, we applied the algorithm for ranking ads in contextual advertising. Our method has shown to significantly improve relevance in offline evaluation and business metrics in online tests in a real large-scale advertising serving system. To scale our computations, we parallelize the algorithm in a MapReduce framework running on Hadoop.
Conference: World Wide Web Conference Series - WWW , pp. 377-386, 2011
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
    • ...This tutorial is based on our recently edited book on multimedia advertising [1], and survey papers on media and text advertising [2, 3]. It is appropriate to both graduate students and senior researchers working in the field of multimedia and/or online advertising, as well as industry practitioners who are working in the field of search engine development, video/image content providers, developers of video/image sharing portals and IPTV ...

    Tao Meiet al. Internet multimedia advertising: techniques and technologies

Sort by: