Hidden Markov model fused with staying time for personalized recommendation
Static model in the recommendation system often regards the user's interest as changeless,which is inconsis-tent with the actual to a certain extent.With regards to this,a hidden Markov model fused with staying time for personal-ized recommendation (ctqHMM) based on the HMM dynamic model is pro...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2014-09-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.09.011/ |
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| _version_ | 1841539731218759680 |
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| author | Sheng-zong LIU Xiao-ping FAN Zhi-fang LIAO Jia HU |
| author_facet | Sheng-zong LIU Xiao-ping FAN Zhi-fang LIAO Jia HU |
| author_sort | Sheng-zong LIU |
| collection | DOAJ |
| description | Static model in the recommendation system often regards the user's interest as changeless,which is inconsis-tent with the actual to a certain extent.With regards to this,a hidden Markov model fused with staying time for personal-ized recommendation (ctqHMM) based on the HMM dynamic model is proposed.The proposed model employs the transfer of the implicit state variables to simulate the changes of Web users' interests,and uses staying time to describe the level of interest to the specific preference and the importance of the recommended pages.Then,a user's clustering method based on the stationary distribution of the ctqHMM is also proposed and applied into the recommending systems.Experiment results on real Web server access log data show the encouraging performance of the proposed method over the state-of-the-arts. |
| format | Article |
| id | doaj-art-5b35711e7de5499e84d8d78373aae9c3 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2014-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-5b35711e7de5499e84d8d78373aae9c32025-01-14T06:44:00ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2014-09-013511212159684187Hidden Markov model fused with staying time for personalized recommendationSheng-zong LIUXiao-ping FANZhi-fang LIAOJia HUStatic model in the recommendation system often regards the user's interest as changeless,which is inconsis-tent with the actual to a certain extent.With regards to this,a hidden Markov model fused with staying time for personal-ized recommendation (ctqHMM) based on the HMM dynamic model is proposed.The proposed model employs the transfer of the implicit state variables to simulate the changes of Web users' interests,and uses staying time to describe the level of interest to the specific preference and the importance of the recommended pages.Then,a user's clustering method based on the stationary distribution of the ctqHMM is also proposed and applied into the recommending systems.Experiment results on real Web server access log data show the encouraging performance of the proposed method over the state-of-the-arts.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.09.011/Web miningclassified time homogeneous hidden Markov modelstationary distributionuser clusteringpersonalized recommendationHMM |
| spellingShingle | Sheng-zong LIU Xiao-ping FAN Zhi-fang LIAO Jia HU Hidden Markov model fused with staying time for personalized recommendation Tongxin xuebao Web mining classified time homogeneous hidden Markov model stationary distribution user clustering personalized recommendation HMM |
| title | Hidden Markov model fused with staying time for personalized recommendation |
| title_full | Hidden Markov model fused with staying time for personalized recommendation |
| title_fullStr | Hidden Markov model fused with staying time for personalized recommendation |
| title_full_unstemmed | Hidden Markov model fused with staying time for personalized recommendation |
| title_short | Hidden Markov model fused with staying time for personalized recommendation |
| title_sort | hidden markov model fused with staying time for personalized recommendation |
| topic | Web mining classified time homogeneous hidden Markov model stationary distribution user clustering personalized recommendation HMM |
| url | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.09.011/ |
| work_keys_str_mv | AT shengzongliu hiddenmarkovmodelfusedwithstayingtimeforpersonalizedrecommendation AT xiaopingfan hiddenmarkovmodelfusedwithstayingtimeforpersonalizedrecommendation AT zhifangliao hiddenmarkovmodelfusedwithstayingtimeforpersonalizedrecommendation AT jiahu hiddenmarkovmodelfusedwithstayingtimeforpersonalizedrecommendation |