Surprise: A Python library for recommender systems
By Nicolas Hug. Journal of Open Source Software, 2020
331 citations recorded by the source corpus.
Recommender systems aim at providing users with a list of recommendations of items that a service offers. For example, a video streaming service will typically rely on a recommender system to propose a personalized list of movies or series to each of its users. A typical problem in recommendation is that of rating prediction: given an incomplete dataset of useritem interations which take the form of numerical ratings (e.g. on a scale from 1 to 5), the goal is to predict the missing ratings for all remaining user-item pairs.
Subfields: Electrical and Electronic Engineering, Information Systems, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/c9b30f6bda58c2ec2a994a56e387221a5765a1ba
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In Electrical and Electronic Engineering
In Information Systems
In Management Science and Operations Research
Citations in the corpus
- FHI Oxford Technical Report # 2018-2 Predicting Human Deliberative Judgments with Machine Learning (2018, methodology): “We applied the standard CF algorithms K-nearest-neighbors (KNN CF) and singular value decomposition (SVD CF), using the implementation in the Surprise library [52].”
- Deep Learning with Gaussian Differential Privacy (2019, unknown): “Table 4 presents the numerical results of our neural networks as well as baseline models in the Suprise library [30] in their default settings.”
- RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms (2020, unknown): “Surprise ([27]) Python 11 3 888 4989 333 2015 No manual”