Open AccessProceedings Article
Librec: a Java library for recommender systems
Guibing Guo,Jie Zhang,Zhu Sun,Neil Yorke-Smith +3 more
- 01 Jan 2015
- Vol. 1388
TL;DR: An open-source Java library that implements a suite of state-of-the-art algorithms as well as a series of evaluation metrics is introduced, empirically finding that LibRec performs faster than other such libraries, while achieving competitive evaluative performance.
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Abstract: 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.
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Citations
Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks
Huan Zhao,Quanming Yao,Jianda Li,Yangqiu Song,Dik Lun Lee +4 more
- 04 Aug 2017
TL;DR: This paper introduces the concept of meta-graph to HIN-based recommendation, and solves the information fusion problem with a "matrix factorization + factorization machine (FM)" approach, and proposes to use FM with Group lasso (FMG) to automatically learn from the observed ratings to effectively select useful meta- graph based features.
652
Controlling Popularity Bias in Learning-to-Rank Recommendation
Himan Abdollahpouri,Robin Burke,Bamshad Mobasher +2 more
- 27 Aug 2017
TL;DR: This paper introduces a flexible regularization-based framework to enhance the long-tail coverage of recommendation lists in a learning-to-rank algorithm and shows that regularization provides a tunable mechanism for controlling the trade-off between accuracy and coverage.
442
Surprise: A Python library for recommender systems
TL;DR: 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.
•Posted Content
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
Wayne Xin Zhao,Shanlei Mu,Yupeng Hou,Zihan Lin,Kaiyuan Li,Yushuo Chen,Yujie Lu,Hui Wang,Changxin Tian,Xingyu Pan,Yingqian Min,Zhichao Feng,Xinyan Fan,Xu Chen,Pengfei Wang,Wendi Ji,Yaliang Li,Xiaoling Wang,Ji-Rong Wen +18 more
TL;DR: A unified, comprehensive and efficient recommender system library called RecBole (pronounced as [rEk'boUl@r]), which provides a unified framework to develop and reproduce recommendation algorithms for research purpose and provides a series of auxiliary functions, tools, and scripts to facilitate the use of this library.
290
Reducing Controversy by Connecting Opposing Views
Kiran Garimella,Gianmarco De Francisci Morales,Aristides Gionis,Michael Mathioudakis +3 more
- 02 Feb 2017
TL;DR: This paper presents a simple model based on a recently-developed user-level controversy score, that is competitive with state-of-the-art link-prediction algorithms and proposes an efficient algorithm that considers only a fraction of all the possible combinations of edges.
References
Similarity vs. Diversity
Barry Smyth,Paul McClave +1 more
- 02 Aug 2001
TL;DR: 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.
492
Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit
Michael D. Ekstrand,Michael Ludwig,Joseph A. Konstan,John Riedl +3 more
- 23 Oct 2011
TL;DR: 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.
204
•Journal Article
PREA: personalized recommendation algorithms toolkit
TL;DR: 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.
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