Understanding and improving automated collaborative filtering systems

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How researchers see this paper

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Explainable Recommendation: A Survey and New Perspectives (2018, background): “Research pioneers in very early stages also noticed the importance of the problem (Herlocker and Konstan, 2000; Herlocker et al., 2000; Sinha and Swearingen, 2002).”
The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system (2010, background): “com) in the recommendation lists, while graphs are utilized mainly in recommendations’ explanations [3, 12].”
A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets (2011, methodology): “However, neither measure has an absolute value that is considered best [64,142,146].”
Item-based collaborative filtering recommendation algorithms (2001, methodology): “A number of different schemes have been proposed to compute the association betwe en i ems ranging from probabilistic approach [6] to more traditional item-item correlations [15, 13].”
An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms (2002, methodology): “We have done empirical experiments that have shown that mean absolute error correlates strongly with many other proposed metrics for collaborative filtering (Herlocker 2000), yet is easier to measure and has well understood significance measures.”