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.
Publisher
International Joint Conferences on Artificial Intelligence Organization
Cited by
5 articles.
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1. A Preference Judgment Tool for Authoritative Assessment;Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval;2023-07-18
2. Search and Discovery in Personal Email Collections;Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining;2022-02-11
3. Search and Discovery in Personal Email Collections;Foundations and Trends® in Information Retrieval;2021
4. Mend the Learning Approach, Not the Data: Insights for Ranking E-Commerce Products;Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track;2021
5. Offline Evaluation by Maximum Similarity to an Ideal Ranking;Proceedings of the 29th ACM International Conference on Information & Knowledge Management;2020-10-19