Recommender Systems Notation: Proposed Common Notation for Teaching and Research

@article{Ekstrand2019RecommenderSN,
  title={Recommender Systems Notation: Proposed Common Notation for Teaching and Research},
  author={Michael D. Ekstrand and Joseph A. Konstan},
  journal={ArXiv},
  year={2019},
  volume={abs/1902.01348}
}
As the field of recommender systems has developed, authors have used a myriad of notations for describing the mathematical workings of recommendation algorithms. These notations ap-pear in research papers, books, lecture notes, blog posts, and software documentation. The dis-ciplinary diversity of the field has not contributed to consistency in notation; scholars whose home base is in information retrieval have different habits and expectations than those in ma-chine learning or human-computer… 
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References

SHOWING 1-10 OF 13 REFERENCES
Collaborative filtering recommender systems
TLDR
This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms.
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.
Evaluating collaborative filtering recommender systems
TLDR
The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Item-based collaborative filtering recommendation algorithms
TLDR
This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
TLDR
This paper proposes a scalable factorization model to incorporate visual signals into predictors of people's opinions, which is applied to a selection of large, real-world datasets and makes use of visual features extracted from product images using (pre-trained) deep networks.
SLIM: Sparse Linear Methods for Top-N Recommender Systems
  • Xia Ning, G. Karypis
  • Computer Science
    2011 IEEE 11th International Conference on Data Mining
  • 2011
TLDR
A novel Sparse Linear Method (SLIM) is proposed, which generates top-N recommendations by aggregating from user purchase/rating profiles and a sparse aggregation coefficient matrix W is learned from SLIM by solving an `1-norm and `2-norm regularized optimization problem.
Teaching Recommender Systems at Large Scale
TLDR
Based on the limited data the authors were able to gather, face-to-face students performed as well as the online-only students or better; they preferred this format to traditional lecture for reasons ranging from pure convenience to the desire to watch videos at a different pace.
BPR: Bayesian Personalized Ranking from Implicit Feedback
TLDR
This paper presents a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem and provides a generic learning algorithm for optimizing models with respect to B PR-Opt.
An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms
TLDR
An analysis framework is applied that divides the neighborhood-based prediction approach into three components and then examines variants of the key parameters in each component, and identifies the three components identified are similarity computation, neighbor selection, and rating combination.
Matrix Factorization Techniques for Recommender Systems
As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of
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