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130
Evaluating recommendation systems
- In Recommender systems handbook
, 2011
"... Abstract Recommender systems are now popular both commercially and in the research community, where many approaches have been suggested for providing recommendations. In many cases a system designer that wishes to employ a rec-ommendation system must choose between a set of candidate approaches. A f ..."
Abstract
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Cited by 85 (2 self)
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Abstract Recommender systems are now popular both commercially and in the research community, where many approaches have been suggested for providing recommendations. In many cases a system designer that wishes to employ a rec-ommendation system must choose between a set of candidate approaches. A
A survey of accuracy evaluation metrics of recommendation tasks
- Journal of Machine Learning Research
"... Recommender systems are now popular both commercially and in the research community, where many algorithms have been suggested for providing recommendations. These algorithms typically perform differently in various domains and tasks. Therefore, it is important from the research perspective, as well ..."
Abstract
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Cited by 48 (0 self)
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recommendation algorithms. The decision on the proper evaluation metric is often critical, as each metric may favor a different algorithm. In this paper we review the proper construction of offline experiments for deciding on the most appropriate algorithm. We discuss three important tasks of recommender systems
Effects of Position Bias on Click-Based Recommender Evaluation
"... Abstract. Measuring the quality of recommendations produced by a recommender system (RS) is challenging. Labels used for evaluation are typically obtained from users of a RS, by asking for explicit feedback, or inferring labels from implicit feedback. Both approaches can introduce significant biases ..."
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and Related Work Recommender systems (RSs) aim to recommend to their users items of interest, such as movies, news articles, or music. Despite the success and popularity of such systems, measuring the quality of an RS is a challenge. In this paper, we examine how bias in user
What recommenders recommend - an analysis of accuracy, popularity, and sales diversity effects
- In Proc. UMAP 2013
, 2013
"... Abstract. In academic studies, the evaluation of recommender system (RS) algorithms is often limited to offline experimental designs based on historical data sets and metrics from the fields of Machine Learning or Information Retrieval. In real-world settings, however, other business-oriented metric ..."
Abstract
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Cited by 3 (1 self)
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Abstract. In academic studies, the evaluation of recommender system (RS) algorithms is often limited to offline experimental designs based on historical data sets and metrics from the fields of Machine Learning or Information Retrieval. In real-world settings, however, other business
Unifying Inconsistent Evaluation Metrics in Recommender Systems
"... Recommender systems are among the most popular tools used by online community these days. Traditionally, recommender techniques were evaluated using accuracy-based metrics such as precision; however, gradually the need for other qualities including more novel and diverse items emerged. Consequently, ..."
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Recommender systems are among the most popular tools used by online community these days. Traditionally, recommender techniques were evaluated using accuracy-based metrics such as precision; however, gradually the need for other qualities including more novel and diverse items emerged. Consequently
Comparison of Recommender System Algorithms focusing on the New-Item and User-Bias Problem
"... Abstract. Recommender systems are used by an increasing number of e-commerce websites to help the customers to find suitable products from a large database. One of the most popular techniques for recommender systems is collaborative filtering. Several collaborative filtering algorithms claim to be a ..."
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Abstract. Recommender systems are used by an increasing number of e-commerce websites to help the customers to find suitable products from a large database. One of the most popular techniques for recommender systems is collaborative filtering. Several collaborative filtering algorithms claim
Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms
- In WSDM
, 2011
"... Contextual bandit algorithms have become popular for online recommendation systems such as Digg, Yahoo! Buzz, and news recommendation in general. Offline evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging du ..."
Abstract
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Cited by 79 (18 self)
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Contextual bandit algorithms have become popular for online recommendation systems such as Digg, Yahoo! Buzz, and news recommendation in general. Offline evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging
Comparative recommender system evaluation: Benchmarking recommendation frameworks
- In Proceedings of the 8th ACM Conference on Recommender Systems, RecSys
, 2014
"... Recommender systems research is often based on comparisons of predictive accuracy: the better the evaluation scores, the better the recommender. However, it is difficult to compare results from dif-ferent recommender systems due to the many options in design and implementation of an evaluation strat ..."
Abstract
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Cited by 8 (3 self)
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Recommender systems research is often based on comparisons of predictive accuracy: the better the evaluation scores, the better the recommender. However, it is difficult to compare results from dif-ferent recommender systems due to the many options in design and implementation of an evaluation
Precision-Oriented Evaluation of Recommender Systems: An Algorithmic Comparison
"... There is considerable methodological divergence in the way precision-oriented metrics are being applied in the Recommender Systems field, and as a consequence, the results reported in different studies are difficult to put in context and compare. We aim to identify the involved methodological design ..."
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Cited by 23 (6 self)
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-of-the-art recommenders, four of the evaluation methodologies are consistent with each other and differ from error metrics, in terms of the comparative recommenders ‟ performance measurements. The other procedure aligns with RMSE, but shows a heavy bias towards known relevant items, considerably overestimating
Dimensions and metrics for . . .
, 2014
"... Abstract recommendation systems support users and developers of various com-puter and software systems to overcome information overload, perform information discovery tasks and approximate computation, among others. They have recently become popular and have attracted a wide variety of application s ..."
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for evaluating recommendation systems. The metrics presented in this chapter are grouped under sixteen different dimensions, e.g., correctness, novelty, coverage. We review these metrics according to the dimensions to which they correspond. A brief overview of approaches to comprehensive evaluation using
Results 1 - 10
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130
