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Web Mining

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第I


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No I


计算机工程与设计

20 年 1
04



OMP E E I E I G ND E I N
UT R
NG NE R N A
D SG

文章编号 :00 0420)1 04 3
1 - 2(040- 0.
07
0 0

Jn 2 4 . 0 a 0

中图分类号 :P9
T 33

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基于 We 挖掘的个性化技术研究 b 冯是聪, 单松魏, 张志刚, 龚笔宏, 李晓明 ( 北京大学 计算机学院,北京 1 81
0 7)

摘 要:针对用户特性向用户提供个性化服务已经成为W b e 技术的研究热点。We 挖掘是实现 W b个性化服务 b e
的关健技术之一。研究了W b e 挖掘技术,阐述了We挖掘技术存在的不足,并对应用W b b e 挖掘技术实现个性化
服务的发展前景进行了分析
关键 词 :个性 化 :We 挖 掘 : 用 户涂稼 :代理 b R s r o pr nlao t ho g s e o w b i e a h e oaztn nl i bs n mn g e c f s ii e c o e a d e in
F N Siog H N n-e Z A G iag O G hn, Xa- i
E G - n, A S g i H N Z - n, N B- g L i mn hc S o w , l g G t io
I o g

(oe oCm u r ne Tc o g P i Uir , jg 81 Ci)
Clg f p eSica e nl y en n ei Bi l 7, n le o t c e n h o , g v s d k y t en 0 i 0 ha A s at T p v e s az s v ef ui o uep f s be a a h s t e t ho g s As n o bt c: r i pr nle e i s s g s r i hs r e c ht oiw b nl i . o e r o d e o id c o n n r l a e e r o p n e o e o r c o e n s c f t ky nl i ow b s aztn wb i idelsd d Te ro i s s g mn g h e t ho g s e pr nlao, mn g ep t i . socmn oui w b i t e e o e f e o i i c e in s y e h ht u g f e in o n w b oaz s v ea s c e ad pr ei s ao l e. epr nle e is i d t epcv a aa z e s id c r p f n h s te r l ny d r e ei e e s
K y rs pr nlao; b i ; em dlg aet e w d: s azt n w mn g u r en; ns o eo i i e in s o i g 1 引



随着 Ie t e的飞速发展, r Wi We 上的网页 nr t n Wod d b l e

量正在呈指数的增长。 og (t/ w .oeo )
Goe : w o l m 搜 l hp/ g g . t w c 索引
擎索引的网页量己
经超过了2亿。
0 根据“
天网” t:
伍t/

/pud.)
e k. un搜索引擎得到的数据,
. e c
目前中国不重复网页
量己超过了30 万。在网页迅速增长的同时, b
00
We 用户
也迅猛地增长。根据C NC n
N I"的最新调查统计,
中国已
有48 万的网络用户, e 已经成为获取信息最重要的
50
Wb
手段之一。海量的网页在为人们提供包罗万象、
无比丰
富的信息资源的同时,
也向人们提出了如何快速从信息
海洋中获取其所需信息的挑战。 前W b

e 系统为所有用
户提供相同的服务,
其典型的服务方式为通过建立一个
Wb
e 站点来向所有用户发布相同的信息,
然而W b e 用户
的需求千差万别,
我们不可能奢望几千万的用户具有相
同的需求。因此,
用户希望W b e 系统能够根据他们特性
的不同提供个性化的服务。事实上,
针对用户特性并向
用户提供个性化服务已经成为 We b技术的一个研究热 同的 W b e 用户通过各种途径访问 W b资源 〔 e 如图 l 所 a 示) 其次,

系统学习用户的特性,
创建用户访问模型(

图 l 所示) 最后, b :
系统根据得到的知识调整服务,
以适
应不同用户的个性化需求(
如图l所示) 因此创建We c 。 b 个性化服务系统的一般步骤为:
①收集用户的各种信息,
如注册信息、
访问历史等;
②分析用户数据,
创建符合用
户特性的访问模式;
③结合用户特性,
向用户提供符合其
特殊需求的个性化服务。
用户对系统提供的服务做出反
馈信息,
系统根据反馈信息调整服务。通过用户与系统
之间循环往复的交互,
系统最终能够为用户提供个性化
服务。从上面的分析可以看出,
通过分析用户的各种信
息建立用户访 问模式是建立个性化系统的关键,
因为只
有首先客观地描述了用户的需求,
然后才能根据这些特
性向用户提供个性化服务。 e 挖掘是实现用户建模的
Wb
基本技术,
我们将在本文第2
节讨论
用户摸型
























所谓We 个性化实质上就是一种以用户需求为中心 b 十一 一


的 We 服务 。图 1 b 描述 了 We 个性化 的实质 。首 先 , b 不

乙e
W扁


We 个性化的实质


基金项目 国
家重点 研究发展规划9 项目
基础

3 基金资助 (1 9376 收稿日 20- -
G9 020 0


期: 0 1 1
224
作者简介:
冯是聪 ( 7一 男,
1 4)
9 ,
河南人,
博士研究生,
主要研究方向为W b e 挖掘和智能检索; 单松巍, 研究生; 张志刚,硕」
硕上
研究生
龚笔宏,硕士研究生; 李晓明,教授,
博士生导师,主要研究方向为W b e 挖掘、
计算机网络与分布式系统
一 4一

万方数据

2 b We 挖掘技术
2 We 挖掘

1 b
We挖掘技术是实现We 个性化服务的核心技术之 b b 一。 b
We 挖掘的一般过程可以分成 3
个阶段: 预处理,


需要对收集的数据进行必要的预处理,
例如清除“
脏”
数据;
②模式发现,
应用不间的W b e 挖掘算法发现用
户访问模式;
③模式分析,
从模式发现的模式集合中
选择有意义的模式。 b
We 挖掘通常可以分成3
大类,
如图2
所示

个性化服务系统。这些系统体应川的W b e 挖掘类型包括
使用挖掘、
内容挖掘和结构挖掘;
收集数据的方式有3
种,
即从客户端、
代理或服务器方得到原始数据。
最后提供的
服务有两类,
即过滤服务和导航服务。表 1
根据这 3个方
面的不同,
比较了基于We 挖掘的典型 W b b e 个性化系统。
表 1 典型 We 个性化系统的比转 b
数据收集方式

系统名称

客户端

代理

挖捆类型

服务器



使用

服 务方式

内容 结构

一丁 ̄ 丁 —
一 一

过涟



导航



图2 b
We 挖桩的分类

W b内容挖掘是从 W b资源中发现信息或知识 e e 的过程。在创建个性化服务系统时,人们通常应用
W b内容挖掘对网页内容进行分析,其中网页的 自动分 e 类技术‘
川在搜索引擎、
数字化图书馆等领域得到了广泛
的应用。
根据实现方法的不同可以分成基于代理的方法

WeV R C b .-

和数据库方法。 e 内容挖掘由于直接处理数据对象的
Wb
内容,
因此得到的结果一般比较精确,
在个性化系统中得
到较广泛的应用。
We 使用挖掘技术通常可以应用到两个领域:当用 b
来分析 W b e 服务器的访问日志时,
可以利用挖掘得到的
服务模型来设计适应性 W b e 站点[ 当应用到单个用户


时,
通过分析用户的访问历史来发现有用的用户访问模
式 ’ W b使用挖掘由于处理数据对象通常为用户的访
0 e

问历史或服务器的访问日志,
无法得知数据对象代表的
内容,
因此得到的结果一般比较粗糙,
但是由于该方法比
较成熟而且实现起来也较内容挖掘简单,
在个性化系统
中也得到了较广泛的应用。 b
We 使用挖掘的基本方法包
括聚类、
关联规则、
序列模式、
分类 、
依赖性建模、
统计分
析等

W七 b结构包括页面内部的结构以及页面之间的结
构。
通过挖掘We 结构信息 对于导航用户浏览行为、 b 改

进站点设计、
评价页面的重要性等都非常重要。a Rn
Pg a e k
算法和C E E
L V R算法利用We 网页间的链接信息来查找 b “
权威" t r e 网页和“
(u ots
A hi ) i 集线器"H b) e 结构挖
(us.
)Wb
掘通常需要整个We 的全局数据, b 因此在个性化搜索引
擎或主题搜索引擎研究领域得到了广泛的应用。
2 典型We 个性化系统的比较

2 b 目前 己经 出现 了多个应用 We 挖掘技术创建的 We b b








, ̄
 ̄响. ... ... ... ,..
..
..
..
.,
.-


目一













3 e 挖掘技术存在的问题 b

尽管W b e挖掘技术已经在W b e个性化系统中得到了 广泛的应用,
但是还存在着以下几个方面的问题。
( 隐私问题 1

这是一个不可回避的问题,因为要想建立个性化 We 系统就必须有用户的参与,同时还要分析用户反馈 b 的信息,
这就可能涉及到用户的隐私。目前的 W b e 个性
化技术还不能很好地解决这个问题,
即在实现个性化服
务的同时而又不侵犯用户的隐私。
( 性能问题 2

Wb e个性化系统都不同程度地扩展了传统的浏览器/
服务器体系结构, e 信息经过相应处理后才返回客户
Wb
端,
就必然会延长响应时间。实时个性化系统对响应时
间要求比较高,
特别是采用中间代理方式的系统川 如果

中间处理过程费时过多或用户数量过大,
系统性能将是

个不可忽视的问题。针对 W b e 系统,
无论是其用户量,
还是系统维护的网页通常都是海量的,目前的We 挖掘 b 算法在处理这些数据时通常都采用离线方式,
因此对于
要求在线实时处理的情况还不能很好地解决
( 质量评价问题 3

应用We 挖掘技术实现We 个性化服务, b b
不同系统
采用不同的We 挖掘技术, b 如何评价它们的建模效果以
及系统最终的服务质量也是一个非常重要的问题。目前
对个性化系统服务质量的评价,
不同系统采用不同的方
式和测试数据,
因此无法评价多个不同个性化系统服务

一 5一

万方数据

质量的优劣。
需要研究一种通用的性能指标和开发相应

参 考 文 献

的B nh a 来评价 We 各种不同的We 挖掘技术 ecm r k b b 4 基于We 挖掘的个性化技术的发展 b ()与人下智能技术的结合 1
个性化系统领域的许多问题最终都可归结到机器学

习、
知识发现等问题上。用户建模过程通常都应用到代
理和多代理技术,
因此人工智能技术与We 挖掘技术的 b 结合将会促进 W b e 个性化系统的飞速发展。
()

2 与交互式多媒体We 技术的结合 b 随着 卜 一 n r t 代 Ie e技术的飞速发展与应用, tn 未来的
We
b的将是多媒体的世界。W b e 个性化技术和We 多媒 b 体系统结合出现了交互式个性化多媒体We 系统1 。 b e 支


持海量多媒体数据流的内容挖掘将成为We挖掘技术的

基本功能之一。
由于这种基于内容的交互式个性化多媒
体 We 系统更能满足用户需要, b 因此也将成为We 个性 b 化系统的发展方向之一
()

3 与数据库等技术的结合
We 挖掘技术的基础是数据挖掘。 b
尽管We 数据由 b 于 自身的特性(
如海量、
半结构、
超链信息等)
使得W七挖 b 掘面临着新的挑战,
但是随着数据库技术特别是数据挖
掘技术的发展,We 挖掘技术也将得到快速的发展。当 b 然,
为解决诸如质量评价、
性能以及隐私问题,
基于W b

挖掘的个性化技术在这些方面也将得到长足的发展。
5 结束语
随着 W b 的迅猛发展, e 千差万别的用户希望We 系 b 统能够根据他们的特性提供个性化的服务。作为实现
We 个性化服务的关键技术之一, b b We挖掘技术受到了广
泛的关注。
但是,
无论 W b e 挖掘技术还是个性化技术,

是综合性很强的新技术,
都需要进一步地完善和发展,

此, 于We挖掘的个性化技术还有着广阔的研究空间。


随着人工智能、
交互式多媒体We 技术的发展, b b We 个性
化系统将会在电子商务、
远程教学、
在线新闻、
搜索引擎
等领域获得更广泛的应用和发展

[ Svsv 1W b g mn gDs vrad lao
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20 . 00

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2 a
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00

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A M ne c o l r i o me e20 . -9 f ne Eet n cm r , 0 1516
C c r o e n c o c c 0


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] bs r ol R S vsv JCe i aate h , e , t . n d v e i a se to h g- s c ti o R s r e g uae a d sr g U L [] P ce is u s b e l en f C. - t h r u o d g o t 19 IE K we e D a ien e 9 E o l g ad tEg e i i s h 9 E n f n d n a n n rg
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9 99

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0.



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H n K n, 1A M 5 1 3 - 0 0 5 g20, 1 81. 8 / / 0. og o 0
C - 3 4 0 10

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1 Y , J e K P B : i e m l a d i
0 u a , . o t x e e en g

v a f Wep e s ctn g C. IK D g c sfao ui V M[] C SG D , o b l i i s S r a a i n A M
20 . 0 2

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1 hp/ wc in ./ vl/ 0-4 h i /L
2 t/ t w m c ec d s2 7 .t E


上接第 3
页)
视的原因之一。除了上述典型应用,贝叶斯网络技术还
可 以用于多种问题的解决和处理。
如用概率网络技术控
制水下无人交通工具的运行、
控制移动机器人的行为等。

3 一0 8

[ CoeG Cm utnl px op bbic -
3 op . pti ac li r aliie
] r o a o o et f it n m y o s f r cun aea bi e r [. f a n l Bysn ento s A i i Iei e e g i e f k J ri l l n s i l w
] tc t- gne1 0( )9- 5 , 233 0. e , 4 : 4 c 9


参 考 文 献: [ C i en D Gi r ekr a D La i B y
4 h krg , g D H ce n ern a,
] c i M e e , m
. n g sn w k iN - dZ. hil o RT - rs Phr[]Tcn aRprMS -R i nto s a a e e c et
[ H ce a DA oao lri wtBysn o s
1 ekr n t rl e n i aea nt r

m . ti n g h i e k u a n w

[ . n a e r -R9 0 Mc s e c c r o M RT - - , r o Rs r ,
Z Tc il t S
]e
h p 5 6 io f e ah t 19 9 5

( 余长慧,


播和平.
贝叶斯网络结构学习二
用于数据挖掘和知
识发现 [ 武汉大学学报信息科学版 〔 .


增刊) 20, )
, 02( :

一 6一

万方数据

9 1. oo er , 4 rsfR sa h 19
47 Mi t e c 9 c [ Ap a RTe i oblf e o - s s t s
5 snl . dsn eeon wr b e y e
] i l h eg f f k a d m i t s f pc f csn J C m u r E tn ni e r ai [. pt ad coiEg e- o re tg ] o e n l r c n r o i e e rg19, 0: 310 ( )6-8 i , 4 2 1 n 9

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