PerSaDoR: Personalized social document representation for improving web search

被引：23

作者：

Bouadjenek, Mohamed Reda ^{[1
]}

Hacid, Hakim ^{[2
]}

Bouzeghoub, Mokrane ^{[3
]}

Vakali, Athena ^{[4
]}

机构：

[1] Univ Melbourne, Dept Comp & Informat Syst, Melbourne, Vic 3010, Australia

[2] Zayed Univ, Acad City, Dubai, U Arab Emirates

[3] Univ Versailles St Quentin En Yvelines UVSQ, DAVID Lab, Versailles, France

[4] Aristotle Univ Thessaloniki, Dept Comp Sci, Thessaloniki, Greece

来源：

INFORMATION SCIENCES | 2016年 / 369卷

关键词：

Information retrieval; Social networks; Social information retrieval; Social search; Social recommendation; PROFILE; FEATURES;

D O I：

10.1016/j.ins.2016.07.046

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we discuss a contribution towards the integration of social information in the index structure of an IR system. Since each user has his/her own understanding and point of view of a given document, we propose an approach in which the index model provides a Personalized Social Document Representation (PerSaDoR) of each document per user based on his/her activities in a social tagging system. The proposed approach relies on matrix factorization to compute the PerSaDoR of documents that match a query, at query time. The complexity analysis shows that our approach scales linearly with the number of documents that match the query, and thus, it can scale to very large datasets. PerSaDoR has been also intensively evaluated by an offline study and by a user survey operated on a large public dataset from delicious showing significant benefits for personalized search compared to state of the art methods. (C) 2016 Elsevier Inc. All rights reserved.

引用

页码：614 / 633

页数：20

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