Learning with Sparse and Biased Feedback for Personal Search
Abstract
Personal search, including email, on-device, and personal media search, has recently attracted a considerable attention from the information retrieval community. In this paper, we provide an overview of challenges and opportunities of learning with implicit user feedback (e.g., click data) in personal search. Implicit user feedback provides a convenient source of supervision for ranking models in personal search. This feedback, however, has two major drawbacks: it is highly sparse and biased due to the personal nature of queries and documents. We demonstrate how these drawbacks can be overcome, and empirically demonstrate the benefits of learning with implicit feedback in the context of a large-scale email search engine.
Cite
Text
Bendersky et al. "Learning with Sparse and Biased Feedback for Personal Search." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/725Markdown
[Bendersky et al. "Learning with Sparse and Biased Feedback for Personal Search." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/bendersky2018ijcai-learning/) doi:10.24963/IJCAI.2018/725BibTeX
@inproceedings{bendersky2018ijcai-learning,
title = {{Learning with Sparse and Biased Feedback for Personal Search}},
author = {Bendersky, Michael and Wang, Xuanhui and Najork, Marc and Metzler, Donald},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {5219-5223},
doi = {10.24963/IJCAI.2018/725},
url = {https://mlanthology.org/ijcai/2018/bendersky2018ijcai-learning/}
}