![]() ![]() ![]() ![]() Within this context, libraries can complement their data by linking it to other, external data sources. The more data are opened on the Web (Open Data), the more integrated sets of data will be connected in the Semantic Web (Linked Open Data). Thus, institutions are highly encouraged to publish, share and interlink their data publicly. The Semantic Web in general and the Linked Open Data Initiative, in particular, are a growing movement for organisations to make their existing data available in a machine-readable format. Additionally, a few recommendations for future investigations on recommending evaluation are proposed. This study highlights the importance of how an evaluation method should be adequately designed and implemented. The results of analysis have shown the difference in evaluation methods in applying different groups of metrics. By using factor analysis, 28 different evaluation metrics were classified into eight groups. It also reveals the popularity order of accuracy metrics (31%) including “Recall, Precision, F-Measure”, “Mean Absolute Error, and Questionnaire studies, Reliability, Accessibility, Feasibility, Usability, Applicability and Performance”. The analysis of variance results shows that offline evaluation methods are more commonly used compared to online and user studies, with the maximum rate of success. We carried out meta-analyses of the evaluation methods and metrics of 67 studies related to context-aware scholarly recommender systems, from the years 2000 to 2014. Researchers of recommender systems have expressed concerns that the evaluation quality cannot be properly judged. With the current growth of the proposed contextual recommending algorithms, evaluating them becomes more critical. ![]()
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