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推荐系统中排名聚合方法的比较研究

A Comparative Study of Rank Aggregation Methods in Recommendation Systems.

作者信息

Bałchanowski Michał, Boryczka Urszula

机构信息

Institute of Computer Science, Faculty of Science and Technology, University of Silesia in Katowice, Będzińska 39, 41-200 Sosnowiec, Poland.

出版信息

Entropy (Basel). 2023 Jan 9;25(1):132. doi: 10.3390/e25010132.

DOI:10.3390/e25010132
PMID:36673273
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9857885/
Abstract

The aim of a recommender system is to suggest to the user certain products or services that most likely will interest them. Within the context of personalized recommender systems, a number of algorithms have been suggested to generate a ranking of items tailored to individual user preferences. However, these algorithms do not generate identical recommendations, and for this reason it has been suggested in the literature that the results of these algorithms can be combined using aggregation techniques, hoping that this will translate into an improvement in the quality of the final recommendation. In order to see which of these techniques increase the quality of recommendations to the greatest extent, the authors of this publication conducted experiments in which they considered five recommendation algorithms and 20 aggregation methods. The research was carried out on the popular and publicly available MovieLens 100k and MovieLens 1M datasets, and the results were confirmed by statistical tests.

摘要

推荐系统的目的是向用户推荐他们最有可能感兴趣的某些产品或服务。在个性化推荐系统的背景下，已经提出了许多算法来生成针对个人用户偏好定制的项目排名。然而，这些算法不会生成完全相同的推荐，因此文献中建议可以使用聚合技术来组合这些算法的结果，希望这能转化为最终推荐质量的提高。为了了解这些技术中哪一种能最大程度地提高推荐质量，本出版物的作者进行了实验，他们考虑了五种推荐算法和20种聚合方法。该研究是在流行的、公开可用的MovieLens 100k和MovieLens 1M数据集上进行的，结果通过统计测试得到了证实。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/126d/9857885/feadc7851188/entropy-25-00132-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/126d/9857885/175dcbadef63/entropy-25-00132-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/126d/9857885/62391ca73c27/entropy-25-00132-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/126d/9857885/ad636fd720cd/entropy-25-00132-g0A3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/126d/9857885/03e7b7a63927/entropy-25-00132-g0A4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/126d/9857885/f857c65f1eb0/entropy-25-00132-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/126d/9857885/feadc7851188/entropy-25-00132-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/126d/9857885/175dcbadef63/entropy-25-00132-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/126d/9857885/62391ca73c27/entropy-25-00132-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/126d/9857885/ad636fd720cd/entropy-25-00132-g0A3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/126d/9857885/03e7b7a63927/entropy-25-00132-g0A4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/126d/9857885/f857c65f1eb0/entropy-25-00132-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/126d/9857885/feadc7851188/entropy-25-00132-g002.jpg

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本文引用的文献

1
A comparative study of rank aggregation methods for partial and top ranked lists in genomic applications.基于基因组学应用的部分和顶级排名列表的等级聚合方法的比较研究。
Brief Bioinform. 2019 Jan 18;20(1):178-189. doi: 10.1093/bib/bbx101.
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Multimodal medical information retrieval with unsupervised rank fusion.基于无监督排序融合的多模态医学信息检索。
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