Mailbox-Based vs. Log-Based Query Completion for Mail Search

Horovitz, Michal; Lewin-Eytan, Liane; Libov, Alex; Maarek, Yoelle; Raviv, Ariel

doi:10.1145/3077136.3080683

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Recent work in the email and personal search domain addresses learning from attributes rather than direct data in order to better generalize [3], leveraging user demographics [8]. A closely-related work generates suggestions using query logs from similar users and settles on a combination of many approaches [16], and recently location has been successfully incorporated into email ranking systems [24].…”

Section: Email and Personal Search Methodsmentioning

confidence: 99%

Semantic Location in Email Query Suggestion

Foley

Zhang

Bendersky

et al. 2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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Mobile devices are pervasive, which means that users have access to web content and their personal documents at all locations, not just their home or office. Existing work has studied how locations can influence information needs, focusing on web queries. We explore whether or not location information can be helpful to users who are searching their own personal documents. We wish to study whether a users' location can predict their queries over their own personal data, so we focus on the task of query suggestion. While we find that using location directly can be helpful, it does not generalize well to novel locations. To improve this situation, we explore using semantic location: that is, rather than memorizing location-query associations, we generalize our location information to names of the closest point of interest. By using short, semantic descriptions of locations, we find that we can more robustly improve query completion and observe that users are already using locations to extend their own queries in this domain. We present a simple but effective model that can use location to predict queries for a user even before they type anything into a search box, and which learns effectively even when not all queries have location information.

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Section: Email and Personal Search Methodsmentioning

confidence: 99%

Semantic Location in Email Query Suggestion

Foley

Zhang

Bendersky

et al. 2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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“…They characterized multiple search strategies and intents, and identified important behavioral differences from web search such as re-finding. Horovitz et al [20] proposed an autocompletion feature for email search, were suggestions are extracted from personal mailbox content in addition to query logs from similar users. Narang et al [30] investigated general email activities and search activities.…”

Section: Related Workmentioning

confidence: 99%

Characterizing Reading Time on Enterprise Emails

Lee

Shokouhi

Dumais

2020

Preprint

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Email is an integral part of people's work and life, enabling them to perform activities such as communicating, searching, managing tasks and storing information. Modern email clients take a step forward and help improve users' productivity by automatically creating reminders, tasks or responses. The act of reading is arguably the only activity that is in common in most -if not all -of the interactions that users have with their emails.In this paper, we characterize how users read their enterprise emails, and reveal the various contextual factors that impact reading time. Our approach starts with a reading time analysis based on the reading events from a major email platform, followed by a user study to provide explanations for some discoveries. We identify multiple temporal and user contextual factors that are correlated with reading time. For instance, email reading time is correlated with user devices: on desktop reading time increases through the morning and peaks at noon but on mobile it increases through the evening till midnight. The reading time is also negatively correlated with the screen size.We have established the connection between user status and reading time: users spend more time reading emails when they have fewer meetings and busy hours during the day. In addition, we find that users also reread emails across devices. Among the cross-device reading events, 76% of reread emails are first visited on mobile and then on desktop. Overall, our study is the first to characterize enterprise email reading time on a very large scale. The findings provide insights to develop better metrics and user models for understanding and improving email interactions.

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“…Other work on personal search employs classical expansion methods in the scenario of query auto-completion or spelling correction. In the absence of query logs, [5,33] rely on email content to extract related terms for query auto-completion. In [14], a learning-to-rank model is trained on features from the query log of an individual user and users with high demographic similarity.…”

Section: Query Expansion For Personal Searchmentioning

confidence: 99%

Multi-view Embedding-based Synonyms for Email Search

Zhang

Bendersky

et al. 2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Synonym expansion is a technique that adds related words to search queries, which may lead to more relevant documents being retrieved, thus improving recall. There is extensive prior work on synonym expansion for web search, however very few studies have tackled its application for email search. Synonym expansion for private corpora like emails poses several unique research challenges. First, the emails are not shared across users, which precludes us from directly employing query-document bipartite graphs, which are standard in web search synonym expansion. Second, user search queries are of personal nature, and may not be generalizable across users. Third, the size of the underlying corpora from which the synonyms may be mined is relatively small (i.e., user's private email inbox) compared to the size of the web corpus. Therefore, in this paper, we propose a solution tailored to the challenges of synonym expansion for email search. We formulate it as a multi-view learning problem, and propose a novel embedding-based model that joins information from multiple sources to obtain the optimal synonym candidates. To demonstrate the effectiveness of the proposed technique, we evaluate our model using both explicit human ratings as well as a live experiment using the Gmail Search service, one of the world's largest email search engines.

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Mailbox-Based vs. Log-Based Query Completion for Mail Search

Cited by 6 publications

References 12 publications

Semantic Location in Email Query Suggestion

Semantic Location in Email Query Suggestion

Characterizing Reading Time on Enterprise Emails

Multi-view Embedding-based Synonyms for Email Search

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