A Content-Based Recommendation System using TrueSkill

被引：2

作者：

Cruz Quispe, Laura ^{[1
]}

Ochoa Luna, Jose Eduardo ^{[1
,2
]}

机构：

[1] San Agustin Natl Univ, Informat Master Program, Arequipa, Peru

[2] San Pablo Catholic Univ, Res & Innovat Ctr Comp Sci, Arequipa, Peru

来源：

2015 FOURTEENTH MEXICAN INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (MICAI) | 2015年

关键词：

Recommendation systems; bayesian networks; content-based;

D O I：

10.1109/MICAI.2015.37

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a probabilistic approach based on TrueSkill for Content-Based Recommendation Systems. On one hand, this proposal allow us to tackle the "cold start" problem because it relies on a content-based approach. On the other hand, it is valuable for handling high uncertainty since it solely depends on available items and ratings given by users. Thus, there is no dependency on the number of items and users. In addition, it is highly scalable because user preferences get richer as items get ranked.

引用

页码：203 / 207

页数：5

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