The Impact of Data Lack and Data Sparsity on the Effectiveness of the Results of the RICeST Journal Finder Results: A Case Study in the Field of Engineering

Several factors are involved in the production and presentation of recommender systems.The aim of this study was to investigate the effect of the two challenges lack and sparsity of data on the effectiveness of the proposed results of the RICeST Journal Finder. The corpus includes more than 15,000 articles from technical and engineering publications in the period 2013 to 2017, which have been collected from their website. Textual elements of these articles were extracted, normalized and processed, and a research body database was created. Based on the number of collected articles, by using Cochran's formula, 400 basic articles that previously published in related to the topic of each journal were selected in a random-proportional method. Title and abstract of these articles as a query, in order to receive the system Journals suggested, to print the article in two stages of before and after improving the two challenges of lack and sparsity of data in the test corpus. The suggested results in each step were saved in Excel. Finally, the effectiveness of the system results in each stage was determined by Leave-one-out Cross-Validation method and based on the accuracy criterion in k.The relative abundance of categories showed that, in the current situation, the target journal was suggested in only 26% of searches in the first 3 ranks. After enriching, normalizing and processing the data and thus improving the lack of data challenge, although 30% of the results were still rated above 10; But the accuracy of the results in the first 3 ranks increased by 15%. Also, after thematically categorizing the data with the aim of improving the sparsity challenge, 30% increase in the accuracy of the system results in the first 3 ranks compared to the previous step was achieved. The results of this study showed that enriching the database, improving the processing process and thematic classification of data in RICeST journal finder can reduce the two challengs lack and sparsity of data and increase the effectiveness of the proposed results of this systems.