|本期目录/Table of Contents|

[1]王卓君,申德荣*,聂铁铮,等. UCM-PPM:基于用户分级的多参量Web预测模型[J].南京大学学报(自然科学),2018,54(1):85.[doi:10.13232/j.cnki.jnju.2018.01.010]
 Wang Zhuojun,Shen Derong*,Nie Tiezheng,et al. UCM-PPM:Multi-parameter web prediction model based on the user classification[J].Journal of Nanjing University(Natural Sciences),2018,54(1):85.[doi:10.13232/j.cnki.jnju.2018.01.010]





 UCM-PPM:Multi-parameter web prediction model based on the user classification
 王卓君申德荣*聂铁铮寇 月于 戈
 Wang ZhuojunShen Derong*Nie TiezhengKou YueYu Ge
 School of Computer Science and Engineering,Northeastern University,Shenyang,110000,China
 Web预取缓 存用户差别化多参量自适应部分匹配预测模型
 Web prefetchingcacheuser differentiationmulti-parameterself-adaption PPM(Prediction by Partial Match)
 With the Web’s rapid development,the demands of low latency and fast response become increasingly urgent over the past few decades.In order to achieve this goal,the prefetching techniques are widely used,where documents are prefetched into caches in advance.Using prefetching techniques,we can avoid network congestion and raise access efficiency.Therefore,an effective prediction model is very essentialin the prefetching technique.Considering the necessities of high accuracy rate and practicability,we use the Prediction by Partial Match(PPM) suffix tree as a fundamental model to predict web pages.We point out some deficiencies on the side of neglect of users’ differences and the metric simplification in current cache-prefetching work.Then we present a multi-parameter web prediction model with a self-adaptation adjustment based on the user hierarchy.The main contents are listed as follows:First,we propose a user classification model based on the history access log in this paper.User behaviors are analyzed and user permutation distribution can be acquired.Then our model classifies users into different categories according to the user contribution degree distribution.The users with different contribution degree account ought to own different weights.In addition,for the users with very low contribution,we align their access web sequences and clusters them.Secondly,a method that sets the node objective function with the multi-parameter effecting is presented to construct the prediction model.The objective function involved with multiple parameters is constructed with elements related to cache replace strategies as the page accessing heat and the user classification accumulation based on the accessing frequency.And we regard the node with maximum value as one owns the strongest predictive ability.We also establish an adjustment mechanism when the prediction tree is working.So the model can learn continuously and adjust dynamically.Finally,we compare our model with several existing models through experiments.Our model has better performance on the prediction accuracy and the cache hit ratio,and we can get better results by adjusting model parameters.


 [1] Padmanabhan V N,Mogul J C.Using predictive prefetching to improve World Wide Web latency.ACM SIGCOMM Computer Communication Review,1996,26(3):22-36.
[2] Xu C Z,Ibrahim T I.Semantics-based personalized prefetching to improve Web performance ∥ The 20th IEEE Conference on Distributed Computing Systems.Piscataway,NJ,USA:IEEE Press,2000:636-643.
[3] Géry M,Haddad H.Evaluation of Web usage mining approaches for user’s next request prediction ∥ The 5th ACM International Workshop on Web Information and Data Management.New Orleans,LA,USA:ACM Press,2003:74-81.
[4] Mabroukeh N R,Ezeife C I.Semantic-rich markov models for web prefetching ∥ Proceedings of the IEEE International Conference on Data Mining Workshops.Miami,FL,USA:IEEE Press,2009:465-470.
[5] Zukerman I,Albrecht D W,Nicholson A E.Predicting users’ requests on the WWW ∥ The 7th International Conference on User Modeling.Banff,Canada:Springer,1999:275-284.
[6] Palpanas T,Mendelzon A.Web prefetching using partial match prediction ∥ The 4th International Web Caching Workshop.San Diego,CA,USA:National Library of Canada,oai:CiteSeerX.psu:,1998.
[7] Su Z,Yang Q,Lu Y,et al.whatNext:A prediction system for web requests using N-gram sequence models ∥ The 1st International Conference on Web Information Systems Engineering.Hong Kong,China:IEEE Press,2000:214-221.
[8] Pitkow J,Pirolli P.Mining longest repeating subsequences to predict world wide web surfing ∥ The 2nd USENIX Symposium on Internet Technologies and Systems.Boulder,CO,USA:USENIX Association Press,1999:13.
[9] Chen X,Zhang X D.Popularity-based PPM:An effective web prefetching technique for high accuracy and low storage ∥ Proceedings of the International Conference on Parallel Processing.Vancouver,Canada:IEEE,2002:296-304.
[10] Deshpande M,Karypis G.Selective Markov models for predicting Web page accesses.ACM Transactions on Internet Technology,2004,4(2):163-184.
[11] Bernhard S D,Leung C K,Reimer V J,et al.Clickstream prediction using sequential stream mining techniques with markov chains ∥ The 20th International Database Engineering & Applications Symposium.Montreal,Canada:ACM Press,2016:24-33.
[12] Gellert A,Florea A.Web prefetching through efficient prediction by partial matching.World Wide Web,2016,19(5):921-932.
[13] Fagni T,Perego R,Silvestri F,et al.Boosting the performance of web search engines:Caching and prefetching query results by exploiting historical usage data.ACM Transactions on Information Systems,2006,24(1):51-78.
[14] Dimopoulos C,Makris C,Panagis Y,et al.A web page usage prediction scheme using sequence indexing and clustering techniques.Data & Knowledge Engineering,2010,69(4):371-382.
[15] Poornalatha G,Raghavendra P S.Web page prediction by clustering and integrated distance measure ∥ Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.Istanbul,Turkey:IEEE Press,2012:1349-1354.
[16] Thiyagarajan R,Thangavel K,Rathipriya R.Recommendation of web pages using weighted k-means clustering.International Journal of Computer Applications,2014,86(14):44-48.
[17] Poornalatha G,Raghavendra P.Alignment based similarity distance measure for better web sessions clustering ∥ The 2nd International Conference on Ambient Systems,Networks and Technologies.Amsterdam,The Netherlands:Elsevier Press,2011:450-457.
[18] Borges J,Levene M.Data mining of user navigation patterns ∥ Proceedings of the International WEBKDD’99 Workshop on Web usage analysis and user profiling.San Diego,CA,USA:Springer,1999:92-111.
[19] Lempel R,Moran S.Predictive caching and prefetching of query results in search engines ∥ The 12th International Conference on World Wide Web.Budapest,Hungary:ACM Press,2003:19-28.
[20] Ban Z J,Gu Z M,Jin Y.A PPM prediction model based on stochastic gradient descent for web prefetching ∥ The 22nd International Conference on Advanced Information Networking and Applications.Okinawa,Japan:IEEE Press,2008:166-173.
[21] Ma H Y,Wang B.User-aware caching and prefetching query results in web search engines ∥ The 35th International ACM SIGIR Conference on Research and Development in Information Retrieval.Portland,OR,USA:ACM Press,2012:1163-1164.
[22] 孟 涛,闫宏飞,王继民.Web网页信息变化的时间局部性规律及其验证.情报学报,2005,24(4):398-406.(Meng T,Yan H F,Wang J M.Characterizing temporal locality in changes of web documents.Journal of the China Society for Scientific and Technical Information,2005,24(4):398-406.)
[23] Wang J Y,Shan S W,Lei M,et al.Web search engine:Characteristics of user behaviors and their implication.Science in China Series:Information Sciences,2001,44(5):351-365.



更新日期/Last Update: 2018-01-31