Open AccessProceedings Article
Librec: a Java library for recommender systems
Guibing Guo,Jie Zhang,Zhu Sun,Neil Yorke-Smith +3 more
- Vol. 1388
TLDR
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.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.read more
Citations
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Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks
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.
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Controlling Popularity Bias in Learning-to-Rank Recommendation
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.
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Surprise: A Python library for recommender systems
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Multi-Rate Deep Learning for Temporal Recommendation
TL;DR: A novel deep neural network based architecture that models the combination of long-term static and short-term temporal user preferences to improve the recommendation performance and a novel pre-train method to reduce the number of free parameters significantly is proposed.
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Reducing Controversy by Connecting Opposing Views
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
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Book ChapterDOI
Similarity vs. Diversity
Barry Smyth,Paul McClave +1 more
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.
Proceedings ArticleDOI
Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit
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.
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.