LinkGuide: Towards a Better Collection of Hyperlinks in a Website Homepage

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1 Proceeding of the World Congre on Engineering 2007 Vol I LinkGuide: Toward a Better Collection of Hyperlink in a Webite Homepage A. Ammari and V. Zharkova chool of Informatic, Univerity of Bradford anammari@bradford.ac.uk, v.v.zharkova@bradford.ac.uk ABTRACT A dramatic and continuou increae in the complexity and ize of webite on the Internet make rather difficult to build uch webite with required information to be eaily found. Thi tudy addree an intelligent approach to deign and implement the homepage of the variou type of webite; uch a commercial, portal, and earch engine webite. In particular, the tudy aim to tudy the problem of how from a large et of hyperlink available in thoe webite, to recommend a mall optimal collection of hyperlink, leading to the required information with the maximum peed and leat effort. The obective of thi hyperlink recommendation i to maximize the efficiency, effectivene, and utilization of the homepage of a Webite. A a reult, LinkGuide a Web Mining baed framework, i propoed to olve thi hyperlink recommendation problem. The tudy dicue an overview of the deign tatu of LinkGuide, and preent an evaluation criterion that will be ued to meaure the performance of the propoed framework. Index Term Hyperlink Recommendation, LinkGuide, Web Mining. I. INTRODUCTION webite where the Internet uer can eaily find information they eek. To addre thi challenge, we formally define a reearch problem in thi area: Hyperlink Recommendation, and then propoe a web mining-baed framework: LinkGuide, a a olution. There are the two maor way that the Internet uer adopt to find the information they eek on the World Wide Web [5]: either by uing earch engine, or by clicking on hyperlink. Reearch on the earch engine focue on improving recall and preciion [5], [2], [3]. Our reearch, however, concentrate on improving the efficiency of the hyperlink baed earching approach. Normally, Web uer click on a erie of hyperlink in order to find the information they eek. Hence, recommending appropriate hyperlink for each one of them in the web page they browe i crucial to improving their earch efficiency and reaching the required webpage. In particular, our reearch focue on placing the appropriate hyperlink in the home page of a webite, the entrance gate to a webite. A the ize and complexity of webite expand dramatically, it ha become increaingly challenging to deign Hyperlink elected from the ite map to the ite homepage Figure : The hyperlink pool (left) and the homepage (right) of the Univerity of Bradford chool of Informatic IBN: WCE 2007

2 Proceeding of the World Congre on Engineering 2007 Vol I A webite homepage conit of hyperlink elected from a hyperlink pool which i a et of hyperlink pointing to toplevel web page [8]. Uually, the hyperlink pool of a webite conit of hyperlink lited in the ite map page or the ite index page. A hown in Fig., hyperlink in the homepage of the Univerity of Bradford chool of Informatic webite ( are elected from it hyperlink pool. The hyperlink pool conit of hyperlink in it ite map page ( Given the webite deign principle that crolling mut be avoided in homepage [5], a well-deigned homepage uually contain around fifty hyperlink. However, the hyperlink pool of a typical Web ite ha at leat everal hundred hyperlink. For example, the homepage of the Univerity of Bradford chool of Informatic webite conit of 58 hyperlink, while the hyperlink pool ha 248 hyperlink. It i computationally too expenive to exhaut all combination of everal dozen hyperlink from a hyperlink pool with everal hundred hyperlink and find the one that i the mot efficient in guiding Web urfer to the information they need. In the chool of Informatic example, the number of combination of electing 58 hyperlink from 248 hyperlink i 2.28E +57. In comparion, our propoed hyperlink recommendation framework, LinkGuide, incorporate both pattern extracted from the tructure of a webite and thoe dicovered from the web erver log file, which record valuable information about the behaviour of the webite uer. LinkGuide firt employ web tructure mining to dicover the hyperlink that lead to eaily find the bigget number of top-level webpage in the ite. econdly, LinkGuide employ web mining technique [0] to extract hidden pattern about hyperlink preference in the webite baed on the navigational behaviour of the webite uer. The ret of the paper i organized a follow: In the next ection we review related work in Web Mining technique from the literature. In ection three we formally define the Hyperlink Recommendation problem baed on our definition of the effectivene, efficiency, and utilization of a homepage; and we give an overview of a Web Mining baed framework, LinkGuide, a a olution; and in ection four we conclude our paper and dicu the future work of LinkGuide implementation. II. RELATED WORK Web mining i defined a a proce of dicovering and analyzing ueful information from the Web. The web data i claified into content data, tructure data, and uage data [20]. A a reult, for each type of the data, a correponding web mining method wa developed. Web content mining i the proce of automatically retrieving, filtering, and categorizing Web document. A good web content mining urvey can be found in [5]. A Web content mining typically make ue of only text on web page, valuable information implicitly contained in hyperlink i overlooked. In contrat, web tructure mining [4] infer ueful pattern from the Web link topology to help retrieve high quality web page. HIT [9] and PageRank [3] are two widely ued web tructure mining algorithm. Web uage mining [20] i the proce of applying data mining technique to dicover web acce pattern from web log file. A web log file i a collection of the data that explicitly record the information related to uer earching behaviour in a web ite. Ueful attribute for the web uage mining in a web log include IP addre, time, and URL, which explicitly decribe who, at what time, acceed which Web page. Additional attribute include tatu of a HTTP requet and the amount of byte returned by a Web erver. Reearch in web uage mining can be claified into two group: general purpoe proect and pecific-purpoe proect. General purpoe proect, uch a [6] and [7], focued on web uage mining in general. An architecture and pecific tep for web uage mining are propoed in [7] that preented a method to identify potentially intereting pattern in uer preference from the mining reult (e.g., pattern in which unlinked web page are viited together frequently). A new data mining capability to mine path traveral pattern from web log i explored in [6]. pecific purpoe proect focued on application of web uage mining. Web uage mining can be ued to improve organization of webite. Adaptive webite proect [6] ued web viiting pattern, learned from web log, to automatically improve the organization and preentation of webite. Web uage mining to meaure and improve the ucce of webite i exploited in [9]. Web uage mining can alo be ued to peronalize uer web urfing experience. In [2], cluter of viitor who exhibited imilar information need (e.g., viitor who acceed imilar Web page) were dicovered via web uage mining. Thee cluter could be ued to claify new viitor and dynamically ugget hyperlink for them. The technique to learn uer preference from web uage data uing data mining technique, uch a aociation rule mining are preented in [4]. Baed on the learned preference, dynamic hyperlink could be recommended for active viiting eion. The algorithm MINPATH i developed in [] for automatic uggetion of ueful hortcut link in real time in order to improve a wirele web navigation. ince our work i mainly aimed to develop a web recommender approach, reearch on the recommendation ytem i much related to thi work. Content-baed filtering and collaborative filtering are approache to realizing recommendation ytem. The content-baed filtering approach ha it root in information retrieval [7]. Recommendation ytem the ue thi approach, uch a NewWeeder [], recommended obect to a uer baed on the comparion between the content of the obect and the IBN: WCE 2007

3 Proceeding of the World Congre on Engineering 2007 Vol I uer profile. Recommendation ytem the ue the collaborative filtering approach, uch a Ringo [8], recommended obect to a uer becaue other uer with imilar tate liked thee obect. The Fab ytem decribed in [2] i baed on the combination of the content-baed filtering approach and the collaborative filtering approach. Reearch that employ only uage information, uch a the work decribed in [6], did not conider the information contained in the tructure of a web ite, which could be valuable in extracting the hyperlink et that lead to the larget number of important page in a webite. On the other ide, [8] gave high preference to each of every two tructurally linked hyperlink if thoe hyperlink are heavily acceed together in the ame eion by the ame uer. uch a high rank for the initial hyperlink that lead to the terminal hyperlink can be exaggerated imply becaue the uer may not need the initial hyperlink at all if the terminal hyperlink wa placed in the ame page of that of the initial hyperlink. Reearch in Web tructure mining that combine the Web content and Web tructure information i alo related to our tudy. For example, HIT value web page that point to many other page that offer good content. Our framework, LinkGuide, alo value the page that point to many other relevant (good) page. However, good page in LinkGuide are meaured uing the web uage information, uch a frequency that the page are viited together. LinkGuide conider both tructurally related and unrelated page. III. OVERVIEW OF THE LINKGUIDE FRAMEWORK A. Problem Definition It wa etablihed [8] that a web homepage i to be deigned to meet the following condition: (a) can effectively facilitate web urfer in locating the information tored in the webite, (b) ha a limited number of hyperlink placed in the homepage, and (c) i frequently viited by web urfer when they earch for information in the Web ite. Therefore, three evaluation metric to meaure the quality of a web homepage are adopted in our tudy: effectivene, efficiency, and utilization, which addre the three previou point (a), (b), and (c). All three metric hould be calculated from Web log file. A web log file can be broken down into eion with each eion repreenting a equence of conecutive web accee by the ame viitor. For the convenience of reader, important notation ued in thi tudy are defined in Table. Effectivene of a homepage i meaured a the degree of eae in finding uer-ought, top-level page from the homepage. We adopt the definition of [8] for the effectivene of a ingle web eion () and the effectivene of a whole web log file (2) a follow: effectiven e( UHL ( ) HL ) = () UHL ( ) effectiven e( ) = effectiven e(log) = (2) The efficiency of a homepage i conidered [8] to be the number of hyperlink the uer clicked in the homepage and in the level two page (page that are pointed by homepage link) to the total number of hyperlink that exit in the homepage. We ee that thi definition leave a poibility that homepage efficiency may till be very high when the uer click only link that exit in level two page without being intereted in hyperlink in the homepage itelf. To provide more accurate reflection to the efficiency of the homepage itelf, we modified the efficiency definition to be the number of hyperlink the uer clicked only in the homepage to the total number of hyperlink that exit in the homepage. The modified eion level efficiency (3) and log file level efficiency (4) are defined a follow: UHL ( ) PHL efficiency ( ) = (3) PHL efficiency ( ) = efficiency (log) = (4) Utilization of a homepage meaure how often a homepage i viited. A well deigned Web ite homepage hould attract uer to utilize it for finding what they eek in other page. Reference [8] meaured the utilization by counting the number of uer-ought, top-level web page that can be eaily found from the homepage. Thi definition include the hyperlink exit not only in the homepage, but alo in the web page that are directly pointed to by the homepage. To give a more accurate reflection to the utilization of the homepage itelf of a webite, utilization in thi tudy will be meaured by counting the number of uer-ought, top-level Web page that directly exit in the homepage, a follow: utilizatio n(log) = UHL ( ) PHL (5) = We formally define the Hyperlink Recommendation problem a follow: Given: () The hyperlink pool (H) of a web ite; (2) The number of hyperlink to be placed in the homepage of the webite (N), where: N < H. Proce: Recommend N hyperlink from the hyperlink pool H to include in the homepage. Obective: Maximize the effectivene, efficiency, and utilization of the homepage. IBN: WCE 2007

4 Proceeding of the World Congre on Engineering 2007 Vol I Notation H UHL ( ) l L PL PHL EHL HL N effectivene ( ) effectivene (log) efficiency ( ) efficiency (log) utilization (log) Table : ummary of Notation ued in the tudy Decription the number of eion in a Web log a eion, for =, 2,, the hyperlink pool of a Web ite uer-ought, top-level Web page in the number of hyperlink in a Web ite a hyperlink, for =, 2,, l the et of hyperlink in the Web page pointed to by L the et of hyperlink in a Web homepage the et of hyperlink in the Web page pointed to by PHL the et of hyperlink in both PHL and EHL = PHL EHL the number of hyperlink to be placed in the homepage effectiven e( effectiven e(log) = UHL ( ) HL ) = UHL ( ) = effectiven e( ) UHL( ) PHL efficiency ( ) = PHL efficiency (log) = utilizatio n(log) = = efficiency ( ) = UHL ( ) PHL B. The LinkGuide Framework There i a need to develop a Web Mining baed olution to olve the hyperlink recommendation problem with the leat poible computational cot. We propoe a Web Mining Baed framework, LinkGuide, to olve the hyperlink recommendation problem. LinkGuide will mainly incorporate data pattern that are extracted from two main type of reource: Data pattern extracted from the tructure of a webite (hyperlink), and data pattern extracted from the log file of the webite. The firt type of data pattern repreent tructure data pattern (The hyperlink hierarchy of a webite), which require a web tructure mining approach to be extracted. The econd type of data pattern repreent uage data pattern (pattern tored in web log file that decribe web urfer navigational behaviour), which require a web uage mining approach to be extracted. Therefore, our propoed mining framework, LinkGuide, will be deigned and implemented to combine the technique of two main web mining method: web tructure and web uage mining. Fig. 2 outline a flow chart of the LinkGuide framework. LinkGuide will firt extract tructural pattern from the hyperlink pool of the webite by paring the page that are pointed by all the hyperlink in the ite map. Thi will generate a lit of hyperlink pair that are tructurally linked. Each pair will contain two hyperlink, Li and L, Li i the initial hyperlink and L i the terminal hyperlink in thi tructure relationhip. LinkGuide will then apply an aociation rule mining technique to web log file to determine hyperlink pair that are heavily acceed together. A heavily acceed hyperlink pair will have a upport value that i larger than a predefined threhold [8]. Baed on the two previouly defined relationhip between hyperlink in a webite: tructure Relationhip, and Acce Relationhip, LinkGuide will categorize hyperlink pair into the following main four group:. Hyperlink pair in Group A: Thi group of hyperlink indicate that both a tructure relationhip and an acce relationhip hold between two hyperlink. Hyperlink relationhip in thi group will be conidered in our approach. 2. Hyperlink in Group B: Thi group of hyperlink indicate that an acce relationhip, but not a tructure relationhip, exit between two hyperlink. Hyperlink relationhip in thi group will be conidered in our approach. IBN: WCE 2007

5 Proceeding of the World Congre on Engineering 2007 Vol I Input Data: H: Hyperlink Pool N: Number of hyperlink in the homepage Dicover hyperlink tructure relationhip Dicover hyperlink acce relationhip Calculate preference of hyperlink baed on Group A relationhip Compare Effectivene, Efficiency, Utilization with hyperlink of other recommendation approache Recommend a et of hyperlink for the homepage of a webite Calculate preference of hyperlink baed on Group B relationhip Output Data: PHL: Recommended et of hyperlink in the homepage Ye Effectivene, Efficiency, Utilization >= Required value No Modify LinkGuide algorithm Figure 2: An abtract Flow Chart of LinkGuide 3. Hyperlink in Group C: Thi group of hyperlink indicate that a tructure relationhip, but not an acce relationhip, exit between two hyperlink. Thi group reveal that the web page pointed to by the initial hyperlink in a tructure relationhip contain a rarelyviited hyperlink, which i the terminal hyperlink, in the tructure relationhip. Hence, a Group C relationhip reveal a deign problem with internal page. A hyperlink recommendation focue on chooing hyperlink for a webite homepage; our approach will not conider hyperlink relationhip in thi group. 4. Hyperlink in Group D: Thi group of hyperlink indicate that there i neither a tructure relationhip, nor an acce relationhip that exit between any two hyperlink. Our approach will not conider hyperlink relationhip in thi group. IV. CONCLUION AND FUTURE WORK In thi paper, we have formally defined a hyperlink recommendation problem and propoe the web mining baed framework, LinkGuide, a a olution. LinkGuide applie the two web mining technique. Web tructure mining i ued to extract pattern related to how hyperlink are connected in the ite map of the webite, wherea web uage mining i ued to extract pattern related to the navigational behaviour of the uer of the webite. Implementation of the LinkGuide framework i needed a a future vindication of the tudy. For the experimental data, we will perform the experiment on the Univerity of Bradford chool of Informatic webite ince it i large and ufficiently generate web log file to allow u to apply the web uage mining proce and do comparion of different hyperlink recommendation approache. REFERENCE [] Anderon, C., Domingo, P., And Weld, D Adaptive Web navigation for wirele device, in Proceeding of the 7th International Joint Conference on Artificial Intelligence, eattle, WA, [2] Balabanovic, M. And hoham, Y Fab: Contentbaed collaborative recommendation, in Commun. ACM 40, 3, [3] Brin,. And Page, L The anatomy of a large-cale hypertextual Web earch engine, in Proceeding of the 7th International World Wide Web Conference, Bribane, Autralia, April. [4] Chakrabarti,., Dom, B. E., Kumar,. R., Raghavan, P., Raagopalan,., Tomkin, A., Gibon D., And Kleinbirg, J Mining the Web link tructure, in IEEE Comput. 32, 8, [5] Chakrabarti, Data mining for hypertext: a tutorial urvey, in ACM IGKDD Exploration, 2,. [6] Chen, M., Park, J., And Yu, P Data mining for path traveral pattern in a Web environment, in Proceeding of the 6th International Conference on Ditributed Computing ytem, Hong Kong, China, May. [7] Cooley, R., Tan, P., And rivatava, J. 999, WebIFT: the Web ite information filter ytem, in Proceeding of IBN: WCE 2007

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