Text-Based User-kNN: Measuring User Similarity Based on Text Reviews

被引：0

作者：

Terzi, Maria ^{[1
]}

Rowe, Matthew ^{[1
]}

Ferrario, Maria-Angela ^{[1
]}

Whittle, Jon ^{[1
]}

机构：

[1] Univ Lancaster, Sch Comp & Commun, InfoLab21, Lancaster LA1 4WA, England

来源：

USER MODELING, ADAPTATION, AND PERSONALIZATION, UMAP 2014 | 2014年 / 8538卷

关键词：

Recommender systems; Collaborative Filtering; Text reviews; Semantic similarity measures;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article reports on a modification of the user-kNN algorithm that measures the similarity between users based on the similarity of text reviews, instead of ratings. We investigate the performance of text semantic similarity measures and we evaluate our text-based user-kNN approach by comparing it to a range of ratings-based approaches in a ratings prediction task. We do so by using datasets from two different domains: movies from RottenTomatoes and Audio CDs from Amazon Products. Our results show that the text-based user-kNN algorithm performs significantly better than the ratings-based approaches in terms of accuracy measured using RMSE.

引用

页码：195 / 206

页数：12

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