東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Dynamic User Profiling for Search Personalisation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dynamic User Profiling for Search Personalisation./
作者:	Vu, Thanh Tien.
面頁冊數:	1 online resource (195 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-06, Section: B.
Contained By:	Dissertations Abstracts International84-06B.
標題:	Intranets. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30181305click for full text (PQDT)
ISBN:	9798358419803

Dynamic User Profiling for Search Personalisation.
Vu, Thanh Tien.

Dynamic User Profiling for Search Personalisation. - 1 online resource (195 pages)

Source: Dissertations Abstracts International, Volume: 84-06, Section: B.

Thesis (Ph.D.)--Open University (United Kingdom), 2017.

Includes bibliographical references

The performance of a personalised search system largely depends upon the ability to build user profiles which accurately capture the user's search interests. However, many approaches to user profiling have neglected the dynamic nature of the user's search interests. That is, a user's search interests typically change in response to their interactions with the search system during the search period. Therefore, a profile built for previous searches might not reflect that user's current search interests.A widely used type of profile represents the topical interests of the user. In these cases, a typical approach is to build a user profile using topics discussed in documents which the user has found relevant, and where the topics are obtained from a human-generated ontology or directory. However, a key limitation of these approaches is that many documents may not contain the topics covered in the ontology. Moreover, the human-generated ontology requires manual effort to determine the correct categories for each document.In this research, we address these problems by proposing novel techniques for dynamically building user profiles which capture the user's search interests changing over time. Instead of using a human-generated ontology, we use a topic modelling technique (Latent Dirichlet Allocation) for unsupervised extraction of the topics from documents. To dynamically build user profiles, we make two important assumptions. First, that the group of users with whom a user shares a set of common interests may be different depending upon the particular topic of interest. Second, the more recently clicked/relevant documents tell us more about the user's current search interests.To test these assumptions, we develop and implement dynamic user profiles, and then evaluate them on two search personalisation tasks. Our first chosen task is personalising search results returned by a Web search engine, and the second is the task of personalising query suggestions made by an Intranet search engine. We found that dynamic user profiles can significantly improve the ranking quality over well-established baselines.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798358419803Subjects--Topical Terms:

755078
Intranets.
Index Terms--Genre/Form:

542853
Electronic books.

Dynamic User Profiling for Search Personalisation.
LDR:03345nmm a2200337K 4500 001 2358158
005 20230725095006.5
006 m o d
007 cr mn ---uuuuu
008 241011s2017 xx obm 000 0 eng d
020 $a 9798358419803
035 $a (MiAaPQ)AAI30181305
035 $a (MiAaPQ)Open_Univ_UK50605
035 $a AAI30181305
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Vu, Thanh Tien. $3 3698694
245 1 0 $a Dynamic User Profiling for Search Personalisation.
264 0 $c 2017
300 $a 1 online resource (195 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-06, Section: B.
500 $a Advisor: Song, Dawei.
502 $a Thesis (Ph.D.)--Open University (United Kingdom), 2017.
504 $a Includes bibliographical references
520 $a The performance of a personalised search system largely depends upon the ability to build user profiles which accurately capture the user's search interests. However, many approaches to user profiling have neglected the dynamic nature of the user's search interests. That is, a user's search interests typically change in response to their interactions with the search system during the search period. Therefore, a profile built for previous searches might not reflect that user's current search interests.A widely used type of profile represents the topical interests of the user. In these cases, a typical approach is to build a user profile using topics discussed in documents which the user has found relevant, and where the topics are obtained from a human-generated ontology or directory. However, a key limitation of these approaches is that many documents may not contain the topics covered in the ontology. Moreover, the human-generated ontology requires manual effort to determine the correct categories for each document.In this research, we address these problems by proposing novel techniques for dynamically building user profiles which capture the user's search interests changing over time. Instead of using a human-generated ontology, we use a topic modelling technique (Latent Dirichlet Allocation) for unsupervised extraction of the topics from documents. To dynamically build user profiles, we make two important assumptions. First, that the group of users with whom a user shares a set of common interests may be different depending upon the particular topic of interest. Second, the more recently clicked/relevant documents tell us more about the user's current search interests.To test these assumptions, we develop and implement dynamic user profiles, and then evaluate them on two search personalisation tasks. Our first chosen task is personalising search results returned by a Web search engine, and the second is the task of personalising query suggestions made by an Intranet search engine. We found that dynamic user profiles can significantly improve the ranking quality over well-established baselines.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Intranets. $3 755078
650 4 $a Customization. $3 3682566
650 4 $a URLs. $3 3681655
650 4 $a User profiles. $3 3698680
650 4 $a Web studies. $3 2122754
650 4 $a Computer science. $3 523869
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0984
690 $a 0646
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Open University (United Kingdom). $3 3427325
773 0 $t Dissertations Abstracts International $g 84-06B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30181305 $z click for full text (PQDT)