Document Type

Conference Proceeding

Publication Date

2018

DOI

10.18122/cs_facpubs/148/boisestate

Abstract

Traditional offline evaluations of recommender systems apply metrics from machine learning and information retrieval in settings where their underlying assumptions no longer hold. This results in significant error and bias in measures of top-N recommendation performance, such as precision, recall, and nDCG. Several of the specific causes of these errors, including popularity bias and misclassified decoy items, are well-explored in the existing literature. In this paper we survey a range of work on identifying and addressing these problems, and report on our work in progress to simulate the recommender data generation and evaluation processes to quantify the extent of evaluation metric errors and assess their sensitivity to various assumptions.

Comments

This paper was presented at The REVEAL 2018 Workshop on Offline Evaluation for Recommender Systems.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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