Recommendation systems(RS)support users and developers of various computer and software systems to overcome information overload, perform information discovery tasks and approximate computation, among others. Recommender systems research is frequently based on comparisons of predictive accuracy: the better the evaluation scores, the better the recommender. However, it is difficult to compare results from different recommender systems due to the many options in design and implementation of an evaluation strategy. Additionally, algorithmic implementations can separate from the standard formulation due to manual tuning and modifications that work better in some situations. It have been compared common recommendation algorithms as implemented in three popular recommendation frameworks. We evaluate the quality of recommender systems, most approaches only focus on the predictive accuracy of these systems. Recent works suggest that beyond accuracy there is a variety of other metrics that should be considered when evaluating a RS. This paper reviews a range of evaluation metrics and measures as well as some approaches used for evaluating recommendation systems. Analysis shows that large differences in recommendation accuracy across frameworks and strategies. we are developing the recommender system for research papers using coverage.
Recommender System, Research Paper Recommender System, Evaluation, Metrics, Coverage.
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