Abstract and 1 Introduction

2 Background and Related work

2.1 Web Scale Information Retrieval

2.2 Existing Datasets

3 MS Marco Web Search Dataset and 3.1 Document Preparation

3.2 Query Selection and Labeling

3.3 Dataset Analysis

3.4 New Challenges Raised by MS MARCO Web Search

4 Benchmark Results and 4.1 Environment Setup

4.2 Baseline Methods

4.3 Evaluation Metrics

4.4 Evaluation of Embedding Models and 4.5 Evaluation of ANN Algorithms

4.6 Evaluation of End-to-end Performance

5 Potential Biases and Limitations

6 Future Work and Conclusions, and References

5 POTENTIAL BIASES AND LIMITATIONS

As discussed in section 3.3.1, The language distribution of documents and queries in the web scenario is high-skewed. This will lead to language bias on data and models. ClueWeb22 [9] demonstrates that there also exists topic distribution skew in the web scenario. Therefore, domain bias also may happen in data and models. To protect user privacy and content health, we remove queries that are rarely triggered (triggered by less than K users, where K is a high value), contain personally identifiable information, offensive content, adult content and queries that have no click connection to the ClueWeb22 document set. As a result, the query distribution is slightly different from the real web query distribution.

Authors:

(1) Qi Chen, Microsoft Beijing, China;

(2) Xiubo Geng, Microsoft Beijing, China;

(3) Corby Rosset, Microsoft, Redmond, United States;

(4) Carolyn Buractaon, Microsoft, Redmond, United States;

(5) Jingwen Lu, Microsoft, Redmond, United States;

(6) Tao Shen, University of Technology Sydney, Sydney, Australia and the work was done at Microsoft;

(7) Kun Zhou, Microsoft, Beijing, China;

(8) Chenyan Xiong, Carnegie Mellon University, Pittsburgh, United States and the work was done at Microsoft;

(9) Yeyun Gong, Microsoft, Beijing, China;

(10) Paul Bennett, Spotify, New York, United States and the work was done at Microsoft;

(11) Nick Craswell, Microsoft, Redmond, United States;

(12) Xing Xie, Microsoft, Beijing, China;

(13) Fan Yang, Microsoft, Beijing, China;

(14) Bryan Tower, Microsoft, Redmond, United States;

(15) Nikhil Rao, Microsoft, Mountain View, United States;

(16) Anlei Dong, Microsoft, Mountain View, United States;

(17) Wenqi Jiang, ETH Zürich, Zürich, Switzerland;

(18) Zheng Liu, Microsoft, Beijing, China;

(19) Mingqin Li, Microsoft, Redmond, United States;

(20) Chuanjie Liu, Microsoft, Beijing, China;

(21) Zengzhong Li, Microsoft, Redmond, United States;

(22) Rangan Majumder, Microsoft, Redmond, United States;

(23) Jennifer Neville, Microsoft, Redmond, United States;

(24) Andy Oakley, Microsoft, Redmond, United States;

(25) Knut Magne Risvik, Microsoft, Oslo, Norway;

(26) Harsha Vardhan Simhadri, Microsoft, Bengaluru, India;

(27) Manik Varma, Microsoft, Bengaluru, India;

(28) Yujing Wang, Microsoft, Beijing, China;

(29) Linjun Yang, Microsoft, Redmond, United States;

(30) Mao Yang, Microsoft, Beijing, China;

(31) Ce Zhang, ETH Zürich, Zürich, Switzerland and the work was done at Microsoft.

This paper is available on arxiv under CC BY 4.0 DEED license.