Corpus ID: 771650

LibRec: A Java Library for Recommender Systems

@inproceedings{Guo2015LibRecAJ,
  title={LibRec: A Java Library for Recommender Systems},
  author={Guibing Guo and Jun-yi Zhang and Zhu Sun and N. Yorke-Smith},
  booktitle={UMAP Workshops},
  year={2015}
}
The large array of recommendation algorithms proposed over the years brings a challenge in reproducing and comparing their performance. This paper introduces an open-source Java library that implements a suite of state-of-the-art algorithms as well as a series of evaluation metrics. We empirically find that LibRec performs faster than other such libraries, while achieving competitive evaluative performance. 
Algorithm Selection with Librec-auto
Due to the complexity of recommendation algorithms, experimentation on recommender systems has become a challenging task. Current recommendation algorithms, while powerful, involve large numbers ofExpand
Automating recommender systems experimentation with librec-auto
Recommender systems research often requires the creation and execution of large numbers of algorithmic experiments to determine the sensitivity of results to the values of various hyperparameters.Expand
Surprise: A Python library for recommender systems
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 proposeExpand
librec-auto: A Tool for Recommender Systems Experimentation
Recommender systems are complex. They integrate the individual needs of users with the characteristics of particular domains of application which may span items from large and potentiallyExpand
Recalot.com: Towards a Reusable, Modular, and RESTFul Social Recommender System
TLDR
This paper presents a concept of a generic reusable RESTful recommender web service framework, designed to perform directly offline and online analysis for research and to use the recommender algorithms in production. Expand
pyRecLab: A Software Library for Quick Prototyping of Recommender Systems
TLDR
Details of pyRecLab are introduced, showing as well performance analysis in terms of error metrics (MAE and RMSE) and train/test time, and it is benchmarked against the popular Java-based library LibRec, showing similar results. Expand
RecoLibry Suite: a set of intelligent tools for the development of recommender systems
TLDR
RecoLibry Suite is presented: a set of intelligent tools to assist different types of users on the development of recommender systems, and the usability of the proposed tools is evaluated with real users. Expand
Query-based simple and scalable recommender systems with apache hivemall
TLDR
This study demonstrates a way to build large-scale recommender systems by just writing a series of SQL-like queries by implementing a variety of recommendation algorithms and common recommendation functions as Hive user-defined functions (UDFs) in Apache Hivemall. Expand
Measuring the Concentration Reinforcement Bias of Recommender Systems
TLDR
A comparative analysis of various RS algorithms illustrating the usefulness of the proposed metrics to accurately measure the concentration reinforcement of recommender systems and the enhancement of the long tail is conducted. Expand
A Review on Datasets and Tools in the Research of Recommender Systems
TLDR
Claims that integrated dataset repositories and search engine for datasets are imperative for advancements of empirical research studies in the field of computer science are claimed. Expand
...
1
2
3
4
5
...

References

SHOWING 1-6 OF 6 REFERENCES
PREA: personalized recommendation algorithms toolkit
TLDR
This paper describes an open-source toolkit implementing many recommendation algorithms as well as popular evaluation metrics, and in contrast to other packages, this toolkit implements recent state-of-the-art algorithms as to most classic algorithms. Expand
Similarity vs. Diversity
TLDR
This paper proposes and evaluates strategies for improving retrieval diversity in CBR systems without compromising similarity or efficiency and argues that often diversity can be as important as similarity. Expand
Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit
TLDR
The utility of LensKit is demonstrated by replicating and extending a set of prior comparative studies of recommender algorithms, and a question recently raised by a leader in the recommender systems community on problems with error-based prediction evaluation is investigated. Expand
Similarity vs
  • diversity. In: Proceedings of the International Conference on Case-Based Reasoning Research and Development. 347–361
  • 2001
MTJ): https://github.com/fommil/matrix-toolkits-java 5 Mahout: https://mahout.apache.org 6 Duine
  • MTJ): https://github.com/fommil/matrix-toolkits-java 5 Mahout: https://mahout.apache.org 6 Duine
Many open-source libraries are available including Mahout 5