Content-based Filtering with Tags: the FIRSt System

被引：2

作者：

Lops, Pasquale ^{[1
]}

de Gemmis, Marco ^{[1
]}

Semeraro, Giovanni ^{[1
]}

Gissi, Paolo ^{[1
]}

Musto, Cataldo ^{[1
]}

Narducci, Fedelucio ^{[1
]}

机构：

[1] Univ Bari Aldo Morof, Dept Comp Sci, I-70126 Bari, Italy

来源：

2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS | 2009年

关键词：

Web; 2.0; Information Filtering; User Modeling; Recommender Systems;

D O I：

10.1109/ISDA.2009.84

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, against the attributes of a content object. This paper describes a content-based recommender system, called FIRSt, that integrates user generated content (UGC) with semantic analysis of content. The main contribution of FIRSt is an integrated strategy that enables a content-based recommender to infer user interests by applying machine learning techniques, both on official item descriptions provided by a publisher and on freely keywords which users adopt to annotate relevant items. Static content and dynamic content are preventively analyzed by advanced linguistic techniques in order to capture the semantics of the user interests, often hidden behind keywords. The proposed approach has been evaluated in the domain of cultural heritage personalization.

引用

页码：255 / 260

页数：6

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