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336
Incorporating Quality Metrics in Centralized/Distributed Information Retrieval on the World Wide Web
, 2000
"... Most information retrieval systems on the Internet rely primarily on similarity ranking algorithms based solely on term frequency statistics. Information quality is usually ignored. This leads to the problem that documents are retrieved without regard to their quality. We present an approach that co ..."
Abstract
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Cited by 61 (0 self)
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Most information retrieval systems on the Internet rely primarily on similarity ranking algorithms based solely on term frequency statistics. Information quality is usually ignored. This leads to the problem that documents are retrieved without regard to their quality. We present an approach
Recommender systems by means of information retrieval
- In WIMS
, 2011
"... ABSTRACT In this paper we present a method for reformulating the Recommender Systems problem in an Information Retrieval one. In our tests we have a dataset of users who give ratings for some movies; we hide some values from the dataset, and we try to predict them again using its remaining portion ..."
Abstract
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Cited by 2 (0 self)
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ABSTRACT In this paper we present a method for reformulating the Recommender Systems problem in an Information Retrieval one. In our tests we have a dataset of users who give ratings for some movies; we hide some values from the dataset, and we try to predict them again using its remaining portion
Information Retrieval and User-Centric Recommender System Evaluation
"... Abstract. Traditional recommender system evaluation focuses on raising the accuracy, or lowering the rating prediction error of the recommendation algorithm. Recently, however, discrepancies between commonly used metrics (e.g. precision, recall, root-mean-square error) and the experienced quality fr ..."
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Abstract. Traditional recommender system evaluation focuses on raising the accuracy, or lowering the rating prediction error of the recommendation algorithm. Recently, however, discrepancies between commonly used metrics (e.g. precision, recall, root-mean-square error) and the experienced quality
Report on the SIGIR 2013 Workshop on Benchmarking Adaptive Retrieval and Recommender Systems
"... Abstract In recent years, immense progress has been made in the development of recommendation, retrieval, and personalisation techniques. The evaluation of these systems is still based on traditional information retrieval and statistics metrics, e.g., precision, recall and/or RMSE, often not taking ..."
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Abstract In recent years, immense progress has been made in the development of recommendation, retrieval, and personalisation techniques. The evaluation of these systems is still based on traditional information retrieval and statistics metrics, e.g., precision, recall and/or RMSE, often
Modelling A User Population for Designing Information Retrieval Metrics
- Proceedings of the Second Workshop on Evaluating Information Access (EVIA 2008
, 2008
"... Although Average Precision (AP) has been the most widely-used retrieval effectiveness metric since the advent of Text Retrieval Conference (TREC), the general belief among researchers is that it lacks a user model. In light of this, Robertson recently pointed out that AP can be interpreted as a spec ..."
Abstract
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Cited by 12 (5 self)
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tend to dig deep into the ranked list, and thereby achieve high reliability. We also demonstrate that one of our new metrics, called NCU gu,β=1, maintains high correlation with AP and shows the highest discriminative power, i.e., the proportion of statistically significantly different system pairs
Statistical power in retrieval experimentation
- Proc. CIKM
, 2008
"... The power of a statistical test specifies the sample size required to reliably detect a given true effect. In IR evaluation, the power corresponds to the number of topics that are likely to be sufficient to detect a certain degree of superiority of one system over another. To predict the power of a ..."
Abstract
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Cited by 14 (2 self)
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is achieved for the same relevance assessment effort by evaluating a large number of topics shallowlythan asmallnumber deeply. Categories andSubjectDescriptors H.3.4 [Information Storage and Retrieval]: Systems and software—performance evaluation.
Exploring Statistical Language Models for Recommender Systems
"... Even though there exist multiple approaches to build recom-mendation algorithms, algebraic techniques based on vec-tor and matrix representations are predominant in the field. Notwithstanding the fact that these algebraic Collaborative Filtering methods have been demonstrated to be very ef-fective i ..."
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-fective in the rating prediction task, they do not generally provide good results in the top-N recommendation task. In this research, we return to the roots of recommender sys-tems and we explore the relationship between Information Filtering and Information Retrieval. We think that proba-bilistic methods taken from
Recommended Citation
, 2007
"... Operational risk assessment of chemical industries by exploiting accident databases ..."
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Operational risk assessment of chemical industries by exploiting accident databases
Towards a Recommender System for Statistical Research Data
"... Abstract. To effectively promote the exchange of scientific data, retrieval services are required to suit the needs of the research community. A large amount of research in the field of economics is based on statistical data, which is often drawn from external sources like data agencies, statistical ..."
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recommendation and notification services for research data sets. In this paper, we elaborate on a case-based recommender system for statistical data, which allows for precise query specification. We discuss required similarity measures on the basis of cross-domain code lists and propose a system architecture
Intent-Oriented Diversity in Recommender Systems
- 34 th Annual Int. ACM Conf. on Research and Development in Information Retrieval (SIGIR 2011
, 2011
"... Diversity as a relevant dimension of retrieval quality is receiving increasing attention in the Information Retrieval and Recommender Systems (RS) fields. The problem has nonetheless been approached under different views and formulations in IR and RS respectively, giving rise to different models, me ..."
Abstract
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Cited by 7 (5 self)
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Diversity as a relevant dimension of retrieval quality is receiving increasing attention in the Information Retrieval and Recommender Systems (RS) fields. The problem has nonetheless been approached under different views and formulations in IR and RS respectively, giving rise to different models
Results 1 - 10
of
336
