Recommender Systems Notation: Proposed Common Notation for Teaching and Research

  title={Recommender Systems Notation: Proposed Common Notation for Teaching and Research},
  author={Michael D. Ekstrand and Joseph A. Konstan},
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… 
Latent Context-aware Recommender Systems
This dissertation will develop a method to extract latent contextual variables and empirically evaluate their usefulness for recommendation and reveal potential in latent contextual approaches.
Neighborhood-Based Collaborative Recommendations: An Introduction
This chapter describes the main components of a generic framework that may be employed for neighborhood-based collaborative recommendations and discusses a few well-known problems associated with the rating data such as sparsity, long-tail, and cold-start problem.
Estimating Error and Bias in Offline Evaluation Results
It is found that missing data in the rating or observation process causes the evaluation protocol to systematically mis-estimate metric values, and in some cases erroneously determine that a popularity-based recommender outperforms even a perfect personalized recommender.


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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
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
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  • Xia Ning, G. Karypis
  • Computer Science
    2011 IEEE 11th International Conference on Data Mining
  • 2011
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.
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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
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.
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