论文标题
使用统计抽样方法，多项式logit模型和模糊推理系统，用于推荐系统的合成数据集生成方法
Synthetic dataset generation methodology for Recommender Systems using statistical sampling methods, a Multinomial Logit model, and a Fuzzy Inference System
论文作者
论文摘要
据说我们生活在数据时代，如果一个人有利用它的工具，数据就无处不在且随时可用。那很可能是真的，但情况却相反。试图启动数据科学项目只是为了找到自己而没有质量数据，这是越来越普遍的。无论是因为没有收集所需的功能，还是由于数据甚至不足，列表还在继续。发生这种情况时，该项目过早地放弃了，或者搜索并使用了类似的数据集。但是，找到一个可以在功能，评级类型等方面满足您需求的数据集可能不是一件容易的事，对于推荐系统而言，尤其是这种情况。在这项工作中，提出了一种用于推荐系统的合成数据集的方法，从而可以克服不容易获得足够可用的质量数据的障碍。使用此方法，可以生成一个合成数据集，以用于由数值/序数和名义特征组成的建议。该数据集是使用高斯Copulas，Dirichlet和Gaussian发行版，多项式logit模型以及模糊的逻辑推理系统构建的，该系统根据不同的用户行为配置文件和感知的项目质量生成评分。
It is said that we live in the age of data, and that data is ubiquitous and readily available if one has the tools to harness it. That may well be true, but so is the opposite. It is ever more common to try to start a data science project only to find oneself without quality data. Be it due to just not having collected the needed features, or due to insufficient data, or even legality issues, the list goes on. When this happens, either the project is prematurely abandoned, or similar datasets are searched for and used. However, finding a dataset that answers your needs in terms of features, type of ratings, etc., may not be an easy task, this is particularly the case for recommender systems. In this work, a methodology for the generation of synthetic datasets for recommender systems is presented, thus allowing to overcome the obstacle of not having quality data in sufficient amount readily available. With this methodology, one can generate a synthetic dataset for recommendation composed by numerical/ordinal and nominal features. The dataset is built with Gaussian copulas, Dirichlet and Gaussian distributions, a Multinomial Logit model and a Fuzzy Logic Inference System that generates the ratings according to different user behavioural profiles and perceived item quality.
