Recommendation Model Based On a Contextual Similarity Measure

被引：0

作者：

Hannech, Amel ^{[1
]}

Adda, Mehdi ^{[2
]}

Mcheick, Hamid ^{[1
]}

机构：

[1] Univ Quebec Chicoutimi, Dept Comp Sci, 555 Blvd Univ, Chicoutimi, PQ G7H 2B1, Canada

[2] Univ Quebec, Math Comp Sci & Engn Dept, 300 Alle Ursulines,CP 3300, Rimouski, PQ G5L 3A1, Canada

来源：

2016 15TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2016) | 2016年

关键词：

collaborative recommendation; preference prediction technique; user profiles; topical ontology; data clustering; contextual similarity; item popularity;

D O I：

10.1109/ICMLA.2016.9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recommendation technique is a personalized search used to assist a user access information/services that are related to his preferences and interests, or to the preferences and interests of similar users. The main challenge of personalized Information Retrieval is the modeling and the integration of user profiles. In this paper, we propose a generic model of user profiles based on the search history of users delimited by several search sessions. These profiles are based on weighted topical graphs and are integrated into a hybrid data recommendation process. To evaluate the proposed system a prototype is developed. The results are quite encouraging; they showed that our model is able to help users when searching for items.

引用

页码：394 / 401

页数：8

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