The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dengan kemunculan Web 2.0, sistem penandaan sosial menjadi sangat popular sejak beberapa tahun kebelakangan ini dan dengan itu membentuk apa yang dipanggil folksonomies. Pengesyoran teg diperibadikan dalam sistem pengetegan sosial adalah untuk menyediakan pengguna senarai peringkat teg untuk sumber khusus yang paling sesuai untuk keperluan pengguna. Banyak pendekatan pengesyoran teg sedia ada menganggap bahawa pengguna adalah bebas dan diedarkan secara sama. Andaian ini mengabaikan hubungan sosial antara pengguna yang semakin popular pada masa kini. Dalam makalah ini, kami menyiasat peranan perhubungan sosial dalam tugas pengesyoran teg dan mencadangkan algoritma penapisan kolaboratif yang diperibadikan. Sebagai tambahan kepada anotasi sosial yang dibuat oleh pengguna kolaboratif, kami menyuntik hubungan sosial antara pengguna dan persamaan kandungan antara sumber ke dalam perwakilan graf folksonomi. Untuk meneroka sepenuhnya struktur graf ini, daripada mengira persamaan antara objek menggunakan vektor ciri, kami mengeksploitasi kaedah pengiraan persamaan secara rawak, yang seterusnya membolehkan kami memodelkan keutamaan teg pengguna dengan persamaan antara pengguna dan semua tag. Kami menggabungkan kedua-dua maklumat kerjasama dan keutamaan teg untuk mengesyorkan teg diperibadikan kepada pengguna. Kami menjalankan percubaan pada set data yang dikumpulkan daripada sistem dunia sebenar. Keputusan eksperimen perbandingan menunjukkan bahawa algoritma yang dicadangkan mengatasi algoritma pengesyoran teg terkini dari segi kualiti ramalan yang diukur dengan ketepatan, ingatan semula dan NDCG.
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Salinan
Kaipeng LIU, Binxing FANG, Weizhe ZHANG, "Exploring Social Relations for Personalized Tag Recommendation in Social Tagging Systems" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 3, pp. 542-551, March 2011, doi: 10.1587/transinf.E94.D.542.
Abstract: With the emergence of Web 2.0, social tagging systems become highly popular in recent years and thus form the so-called folksonomies. Personalized tag recommendation in social tagging systems is to provide a user with a ranked list of tags for a specific resource that best serves the user's needs. Many existing tag recommendation approaches assume that users are independent and identically distributed. This assumption ignores the social relations between users, which are increasingly popular nowadays. In this paper, we investigate the role of social relations in the task of tag recommendation and propose a personalized collaborative filtering algorithm. In addition to the social annotations made by collaborative users, we inject the social relations between users and the content similarities between resources into a graph representation of folksonomies. To fully explore the structure of this graph, instead of computing similarities between objects using feature vectors, we exploit the method of random-walk computation of similarities, which furthermore enable us to model a user's tag preferences with the similarities between the user and all the tags. We combine both the collaborative information and the tag preferences to recommend personalized tags to users. We conduct experiments on a dataset collected from a real-world system. The results of comparative experiments show that the proposed algorithm outperforms state-of-the-art tag recommendation algorithms in terms of prediction quality measured by precision, recall and NDCG.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.542/_p
Salinan
@ARTICLE{e94-d_3_542,
author={Kaipeng LIU, Binxing FANG, Weizhe ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Exploring Social Relations for Personalized Tag Recommendation in Social Tagging Systems},
year={2011},
volume={E94-D},
number={3},
pages={542-551},
abstract={With the emergence of Web 2.0, social tagging systems become highly popular in recent years and thus form the so-called folksonomies. Personalized tag recommendation in social tagging systems is to provide a user with a ranked list of tags for a specific resource that best serves the user's needs. Many existing tag recommendation approaches assume that users are independent and identically distributed. This assumption ignores the social relations between users, which are increasingly popular nowadays. In this paper, we investigate the role of social relations in the task of tag recommendation and propose a personalized collaborative filtering algorithm. In addition to the social annotations made by collaborative users, we inject the social relations between users and the content similarities between resources into a graph representation of folksonomies. To fully explore the structure of this graph, instead of computing similarities between objects using feature vectors, we exploit the method of random-walk computation of similarities, which furthermore enable us to model a user's tag preferences with the similarities between the user and all the tags. We combine both the collaborative information and the tag preferences to recommend personalized tags to users. We conduct experiments on a dataset collected from a real-world system. The results of comparative experiments show that the proposed algorithm outperforms state-of-the-art tag recommendation algorithms in terms of prediction quality measured by precision, recall and NDCG.},
keywords={},
doi={10.1587/transinf.E94.D.542},
ISSN={1745-1361},
month={March},}
Salinan
TY - JOUR
TI - Exploring Social Relations for Personalized Tag Recommendation in Social Tagging Systems
T2 - IEICE TRANSACTIONS on Information
SP - 542
EP - 551
AU - Kaipeng LIU
AU - Binxing FANG
AU - Weizhe ZHANG
PY - 2011
DO - 10.1587/transinf.E94.D.542
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2011
AB - With the emergence of Web 2.0, social tagging systems become highly popular in recent years and thus form the so-called folksonomies. Personalized tag recommendation in social tagging systems is to provide a user with a ranked list of tags for a specific resource that best serves the user's needs. Many existing tag recommendation approaches assume that users are independent and identically distributed. This assumption ignores the social relations between users, which are increasingly popular nowadays. In this paper, we investigate the role of social relations in the task of tag recommendation and propose a personalized collaborative filtering algorithm. In addition to the social annotations made by collaborative users, we inject the social relations between users and the content similarities between resources into a graph representation of folksonomies. To fully explore the structure of this graph, instead of computing similarities between objects using feature vectors, we exploit the method of random-walk computation of similarities, which furthermore enable us to model a user's tag preferences with the similarities between the user and all the tags. We combine both the collaborative information and the tag preferences to recommend personalized tags to users. We conduct experiments on a dataset collected from a real-world system. The results of comparative experiments show that the proposed algorithm outperforms state-of-the-art tag recommendation algorithms in terms of prediction quality measured by precision, recall and NDCG.
ER -