André Vellino and Inge Alberts
This paper aims to investigate how automatic classification can assist employees and records managers with the appraisal of e-mails as records of value for the organization.
Abstract
Purpose
This paper aims to investigate how automatic classification can assist employees and records managers with the appraisal of e-mails as records of value for the organization.
Design/methodology/approach
The study performed a qualitative analysis of the appraisal behaviours of eight records management experts to train a series of support vector machine classifiers to replicate the decision process for identifying e-mails of business value. Automatic classification experiments were performed on a corpus of 846 e-mails from two of these experts’ mailboxes.
Findings
Despite the highly contextual nature of record value, these experiments show that classifiers have a high degree of accuracy. Unlike existing manual practices in corporate e-mail archiving, machine classification models are not highly dependent on features such as the identity of the sender and receiver or on threading, forwarding or importance flags. Rather, the dominant discriminating features are textual features from the e-mail body and subject field.
Research limitations/implications
The need to automatically classify corporate e-mails is growing in importance, as e-mail remains one of the prevalent recordkeeping challenges.
Practical implications
Automated methods for identifying e-mail records promise to be of significant benefit to organizations that need to appraise e-mail for long-term preservation and access on demand.
Social implications
The research adopts an innovative approach to assist employees and records managers with the appraisal of digital records. By doing so, the research fosters new insights on the adoption of technological strategies to automate recordkeeping tasks, an important research gap.
Originality/value
Our experiment show that a SVM classifier can be trained to replicate an expert's decision process for identifying e-mails of business value with a reasonably high degree of accuracy. In principle, such a classifier could be integrated into a corporate Electronic Document and Records Management System (EDRMS) to improve the quality of e-mail records appraisal.
Details
Keywords
The purpose of this paper is to present an empirical comparison between the recommendations generated by a citation-based recommender for research articles in a digital library…
Abstract
Purpose
The purpose of this paper is to present an empirical comparison between the recommendations generated by a citation-based recommender for research articles in a digital library with those produced by a user-based recommender (ExLibris “bX”).
Design/methodology/approach
For these computer experiments 9,453 articles were randomly selected from among 6.6 M articles in a digital library as starting points for generating recommendations. The same seed articles were used to generate recommendations in both recommender systems and the resulting recommendations were compared according to the “semantic distance” between the seed articles and the recommended ones, the coverage of the recommendations and the spread in publication dates between the seed and the resulting recommendations.
Findings
Out of the 9,453 test runs, the recommendation coverage was 30 per cent for the user-based recommender vs 24 per cent for the citation-based one. Only 12 per cent of seed articles produced recommendations with both recommenders and none of the recommended articles were the same. Both recommenders yielded recommendations with about the same semantic distance between the seed article and the recommended articles. The average differences between the publication dates of the recommended articles and the seed articles is dramatically greater for the citation-based recommender (+7.6 years) compared with the forward-looking user-based recommender.
Originality/value
This paper reports on the only known empirical comparison between the Ex Librix “bX” recommendation system and a citation-based collaborative recommendation system. It extends prior preliminary findings with a larger data set and with an analysis of the publication dates of recommendations for each system.