Generating Synthetic Data for Context-Aware Recommender Systems

Pasinato, Marden B.; Mello, Carlos E.; Aufaure, Marie-Aude; Zimbrão, Geraldo

doi:10.1109/brics-cci-cbic.2013.99

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…The work generates learning rules based on the attributes entered by the user to build relationships between these attributes, and the technique used for the data was decision tree algorithms. Other similar works have appeared in the literature proposing data synthesizers for testing in data mining tools [15], [16] [17]. These works generate data for testing in data mining tools since obtaining real data can be very costly or limited by privacy rights.…”

Section: Related Workmentioning

confidence: 99%

Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools

et al. 2020

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Data generators are applications that produce synthetic datasets, which are useful for testing data analytics applications, such as machine learning algorithms and information visualization techniques. Each data generator application has a different approach to generate data. Consequently, each one has functionality gaps that make it unsuitable for some tasks (e.g., lack of ways to create outliers and non-random noise). This paper presents a data generator application that aims to fill relevant gaps scattered across other applications, providing a flexible tool to assist researchers in exhaustively testing their techniques in more diverse ways. The proposed system allows users to define and compose known statistical distributions to produce the desired outcome, visualizing the behavior of the data in real-time to analyze if it has the characteristics needed for efficient testing. This paper presents in detail the tool functionalities and how to create datasets, as well as a usage scenario to illustrate the process of data creation. INDEX TERMS Synthetic dataset generator, benchmark datasets creation, data creation system.

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Section: Related Workmentioning

confidence: 99%

Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools

et al. 2020

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“…The focus on synthetic data has been in research on contextaware recommendation. In [17], the authors proposed an abstract methodology for context-aware collection of data (in terms of item ratings and context of attributes). In [27], the authors built a methodology to generate synthetic data sets for evaluating attribute-aware recommender systems.…”

Section: Data Synthesis For Recommendationmentioning

confidence: 99%

Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding

Slokom,

Larson,

Hanjalic

2020

Preprint

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This paper demonstrates the potential of statistical disclosure control for protecting the data used to train recommender systems. Specifically, we use a synthetic data generation approach to hide specific information in the user-item matrix. We apply a transformation to the original data that changes some values, but leaves others the same. The result is a partially synthetic data set that can be used for recommendation, but contains less specific information about individual user preferences. Synthetic data has a potential to be useful for companies, who are interested in releasing data to allow outside parties to develop new recommender algorithms, i.e., in the case of a recommender system challenge, and also reducing the risks associated with data misappropriation. Our experiments run a set of recommender system algorithms on our partially synthetic data sets as well as on the original data. The results show that the relative performance of the algorithms on the partially synthetic data reflects the relative performance on the original data. Further analysis demonstrates that properties of the original data are preserved under synthesis, but that for certain examples of attributes accessible in the original data are hidden in the synthesized data 3 .

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“…The metric of information entropy is then exploited to control the randomness of the synthetic data. A similar method has been discussed by Pasinato et al [13]: their intuition is to represent the heterogeneous rating behaviors of the users with different statistical distributions.…”

Section: Related Workmentioning

confidence: 99%

All You Need is Ratings: A Clustering Approach to Synthetic Rating Datasets Generation

Monti,

Rizzo,

Morisio

2019

Preprint

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The public availability of collections containing user preferences is of vital importance for performing offline evaluations in the field of recommender systems. However, the number of rating datasets is limited because of the costs required for their creation and the fear of violating the privacy of the users by sharing them. For this reason, numerous research attempts investigated the creation of synthetic collections of ratings using generative approaches. Nevertheless, these datasets are usually not reliable enough for conducting an evaluation campaign. In this paper, we propose a method for creating synthetic datasets with a configurable number of users that mimic the characteristics of already existing ones. We empirically validated the proposed approach by exploiting the synthetic datasets for evaluating different recommenders and by comparing the results with the ones obtained using real datasets.

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Generating Synthetic Data for Context-Aware Recommender Systems

Cited by 11 publications

References 7 publications

Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools

Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools

Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding

All You Need is Ratings: A Clustering Approach to Synthetic Rating Datasets Generation

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