MyMediaLite: a free recommender system library

By Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme. 2011

435 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research, Recommender Systems

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Citations in the corpus

Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering (2016, methodology): “Most of our baselines are from MyMediaLite [9].”
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback (2015, methodology): “• MM-MF: A pairwise MF model from Gantner et al. (2011), which is optimized for a hinge ranking loss on x uij and trained using SGA as in BPR-MF.”
TFMAP: optimizing MAP for top-n context-aware recommendation (2012, methodology): “Note that the implementation of this baseline is done with the publicly available software MyMediaLite [11].”
One-class collaborative filtering with random graphs (2013, neutral): “[6, 21].”
Learning Tree-based Deep Model for Recommender Systems (2018, methodology): “Implementation of BPR-MF provided by [10] is used.”
Collaborative Knowledge Base Embedding for Recommender Systems (2016, methodology): “Then we perform a SGD update for each parameter using the gradient of the corresponding objective function [10].”
Evaluating the Quality of Learning Resources: A Learnersourcing Approach (2021, result): “of MyMediaLite [64] for all of our conducted studies.”
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms (2020, background): “also useful to standardize the evaluation protocol of recommendation algorithms. Indeed, a considerable number of recommender system libraries have been released in the past decade (Guo et al., 2015; Gantner et al., 2011; Wu et al., 2017; Wang et al., 2020a; Sun et al., 2020). These works have large”
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering (2012, methodology): “Note that the implementation of this baseline is done with the publicly available software MyMediaLite [10].”
Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios (2017, methodology): “In our experiments, we compared our approach with the implementation of BPRSLIM [19, 25] available in MyMediaLite4 [9] as”
Collaborative Filtering with Recurrent Neural Networks (2016, methodology): “We used the original implementation of BPR-MF, available in the MyMediaLite framework [5].”
Multi-relational matrix factorization using bayesian personalized ranking for social network data (2012, methodology): “The code for BPR-map is available in the MyMediaLite framework [2].”
Long and Short-Term Recommendations with Recurrent Neural Networks (2017, methodology): “We used the original implementation of BPR-MF, available in the MyMediaLite framework [5].”
Computing Recommendations via a Knowledge Graph-aware Autoencoder (2018, methodology): “We used the implementations of BPRMF and WRMF available in MyMediaLite8 [16] and implemented the autoencoder in Keras9.”
Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction (2014, methodology): “All these baseline methods are implemented by using the MyMediaLite library [9].”
Collaborative Metric Learning (2017, methodology): “We validate our implementation of the baselines by comparing the results to that from [16, 46, 12] All hyperparameters are tuned to perform the best on the validation set.”