This study compares some of the behavioural characteristics of two recommender systems for scholarly articles in a digital library: a usage-based recommender and an experimental citation-based recommender. Experimental results show that article recommendations based only on usage data are slightly better at solving the perennial data-sparsity problem that plagues collaborative filtering recommenders in digital libraries. However, citation-based recommendations are more semantically diverse and have less in common with conventional search results than the usage-based method. However both of these methods are complementary since most of the time if one recommender produces a list of recommendations the other does not.

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.

Some research suggests that a significant number of Generation Z teenagers (those born in the late 1990s or early 2000s) display an insufficient level of information literacy (IL) to function effectively in an information-based society. Yet many of them are gamers who succeed at accomplishing game-related tasks that require a number of IL skills such as information seeking, the critical assessment of sources and relevance ranking of information. This paper describes the results of an interpretive case study of the information behaviours of teenage gamers that supports the hypothesis that the online game Minecraft supports the development of such IL skills. The online interactions of 510 participants of a public discussion forum on Minecraft and interviews from eight teenage Minecraft gamers, as well as the game itself, were analysed. This study suggest that some aspects of Minecraft’s design effectively induce players to seek out game-related information in affinity spaces (online informal learning spaces), select appropriate sources, evaluate the information shared by fellow gamers and decide which information best satisfies their needs.

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.