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1	KNOWLEDGE COPRODUCTION IN DISCOGS MUSIC DATABASE : A study of the motivations behind a crowdsourced online discography Sicilia, Maria January 2020 (has links) Discogs is a crowdsourced online discography that has become one the largest music databases and marketplace used by collectors and enthusiasts. To learn about what motivates Discogs community users to contribute, the answers provided by the respondents(n=135) to an online survey with Likert-scaled items measuring different types of motivations and some open-ended questions were analysed. The results suggested that Discogs contributors are primarily driven by altruistic reasons (intrinsic motivation) followed by pragmatism (extrinsic motivation). While sellers contributed to the database mostly to sell in the Marketplace, they were equally motivated by intrinsic factors, with similar rates to respondents who did not have economic interests in the website. Open-ended questions indicated that conflicts with other users could decrease the motivation to contribute. In addition, respondents revealed that during their trajectory ascontributors, intrinsic motivation increased over time. Also, experience and expertise were the reasons why some contributors had roles recognised by other members in the Discogs community Discogs Online knowledge coproduction Motivation Contribution Crowdsourcing Open source content Information Studies Biblioteks- och informationsvetenskap
2	Price Prediction of Vinyl Records Using Machine Learning Algorithms Johansson, David January 2020 (has links) Machine learning algorithms have been used for price prediction within several application areas. Examples include real estate, the stock market, tourist accommodation, electricity, art, cryptocurrencies, and fine wine. Common approaches in studies are to evaluate the accuracy of predictions and compare different algorithms, such as Linear Regression or Neural Networks. There is a thriving global second-hand market for vinyl records, but the research of price prediction within the area is very limited. The purpose of this project was to expand on existing knowledge within price prediction in general to evaluate some aspects of price prediction of vinyl records. That included investigating the possible level of accuracy and comparing the efficiency of algorithms. A dataset of 37000 samples of vinyl records was created with data from the Discogs website, and multiple machine learning algorithms were utilized in a controlled experiment. Among the conclusions drawn from the results was that the Random Forest algorithm generally generated the strongest results, that results can vary substantially between different artists or genres, and that a large part of the predictions had a good accuracy level, but that a relatively small amount of large errors had a considerable effect on the general results. price prediction price estimation vinyl records vinyl prices regression machine learning machine learning algorithms algorithm comparison dataset vinyl dataset k-nearest neighbors linear regression neural network random forest discogs ai artificial intelligence prisuppskattning vinyl vinylskivor maskininlärning artificiell intelligens Computer Sciences Datavetenskap (datalogi)

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KNOWLEDGE COPRODUCTION IN DISCOGS MUSIC DATABASE : A study of the motivations behind a crowdsourced online discography

Price Prediction of Vinyl Records Using Machine Learning Algorithms