Exploring memory in email refinding

Elsweiler, David; Baillie, Mark; Ruthven, Ian

doi:10.1145/1402256.1402260

Cited by 41 publications

(33 citation statements)

References 58 publications

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“…user recollections [10]). The lack of research on email re-finding contrasts that of files and web pages, from which we know about preferences for browse over search in different scenarios [3,4]; particular seeking tactics [24,6]; that re-finding tends to be performed in bursts [25]; that strategies are honed over time for regularly refound objects [25] and that different behavior is exhibited for within and between session searches [25].…”

Section: Background Literaturementioning

confidence: 99%

“…It may, therefore, be worthwhile highlighting messages that have recently been re-found to make them easier to locate or by providing a means to filter to these messages, given that people are normally able to identify when they last accessed a message [10]. A useful extension of this work would be try and predict more accurately which messages might be likely to be re-found again.…”

Section: Design Implicationsmentioning

confidence: 99%

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Understanding re-finding behavior in naturalistic email interaction logs

Elsweiler

Harvey

Häcker

2011

Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

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In this paper we present a longitudinal, naturalistic study of email behavior (n=47) and describe our efforts at isolating re-finding behavior in the logs through various qualitative and quantitative analyses. The presented work underlines the methodological challenges faced with this kind of research, but demonstrates that it is possible to isolate refinding behavior from email interaction logs with reasonable accuracy. Using the approaches developed we uncover interesting aspects of email re-finding behavior that have so far been impossible to study, such as how various features of email-clients are used in re-finding and the difficulties people encounter when using these. We explain how our findings could influence the design of email-clients and outline our thoughts on how future, more in depth analyses, can build on the work presented here to achieve a fuller understanding of email behavior and the support that people need.

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Section: Background Literaturementioning

confidence: 99%

Section: Design Implicationsmentioning

confidence: 99%

Understanding re-finding behavior in naturalistic email interaction logs

Elsweiler

Harvey

Häcker

2011

Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…Re-finding behaviour is guided by user recollections [6] and people remember different things in different situations, with this being heavily influenced by contextual factors [13]. We hypothesize that the types of queries submitted will change in different situations.…”

Section: Evaluating Personal Searchmentioning

confidence: 99%

Seeding simulated queries with user-study data for personal search evaluation

Elsweiler

Losada

Toucedo

et al. 2011

Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

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In this paper we perform a lab-based user study (n=21) of email re-finding behaviour, examining how the characteristics of submitted queries change in different situations. A number of logistic regression models are developed on the query data to explore the relationship between userand contextual-variables and query characteristics including length, field submitted to and use of named entities. We reveal several interesting trends and use the findings to seed a simulated evaluation of various retrieval models. Not only is this an enhancement of existing evaluation methods for Personal Search, but the results show that different models are more effective in different situations, which has implications both for the design of email search tools and for the way algorithms for Personal Search are evaluated.

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“…Digital Object Re-finding The main focus of the Digital object refinding is on the storage and search of collections of heterogeneous digital objects. These objects are either generated by the users or been encountered at the time of work processes, like emails, documents etc [4], [5], [6] . Metadata on the diverse aspects of an object"s context like, when, where and relationships amongst them, is used commonly by tools.…”

Section: Literature Reviewmentioning

confidence: 99%

Review on Memory Recall via Image Processing Intelligent Using Social Contacts Based on Eigen Value Implementation

Gulhane¹,

Kapse²

2016

International Journal of Advanced Research in Computer and Comm

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Human memory very often fails. People are frequently beset with questions like "Who is that person? I think I met him/her last year." Available memory systems cannot well support the recall of names effectively. This paper explores the memory recall enhancement problem from the perspective of memory cue extraction and associative search, and proposes a generic method to extract memory cues from heterogeneous, multimodal, physical/virtual data sources. Specifically, we can use the contact name recall in the academic community as the target application to showcase our proposed methodology. We next develop an intelligent social contact manager that supports 1) auto collection of rich contact data from a combination of various pervasive sensors and Web data sources, and 2) associative search of contacts when human memory fails. The proposed system is validated with the help of testing the performance of contact data collection techniques. An user study on contact memory recall is conducted, through which several findings about contact memorizing and recall are presented. Various Classic cognitive psychology theories are used to interpret these findings [1].

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Exploring memory in email refinding

Cited by 41 publications

References 58 publications

Understanding re-finding behavior in naturalistic email interaction logs

Understanding re-finding behavior in naturalistic email interaction logs

Seeding simulated queries with user-study data for personal search evaluation

Review on Memory Recall via Image Processing Intelligent Using Social Contacts Based on Eigen Value Implementation

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