A framework for dataset benchmarking and its application to a new movie rating datasetReport as inadecuate

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(2016)ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY.7(3). Mark abstract Rating datasets are of paramount importance in recommender systems research. They serve as input for recommendation algorithms, as simulation data, or for evaluation purposes. In the past, public accessible rating datasets were not abundantly available, leaving researchers no choice but to work with old and static datasets like MovieLens and Netflix. More recently, however, emerging trends as social media and smart-phones are found to provide rich data sources which can be turned into valuable research datasets. While dataset availability is growing, a structured way for introducing and comparing new datasets is currently still lacking. In this work, we propose a five-step framework to introduce and benchmark new datasets in the recommender systems domain. We illustrate our framework on a new movie rating dataset-called Movie Tweetings-collected from Twitter. Following our framework, we detail the origin of the dataset, provide basic descriptive statistics, investigate external validity, report the results of a number of reproducible benchmarks, and conclude by discussing some interesting advantages and appropriate research use cases.

Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8163552

Author: Simon Dooms, Alejandro Bellogin, Toon De Pessemier and Luc Martens

Source: https://biblio.ugent.be/publication/8163552


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