A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm.
Prediction of user future movements and intentions based on the users’ clickstream data is a main challenging problem in Web based recommendation systems. Web usage mining based on the users’ clickstream data has become the subject of exhaustive research, as its potential for web based personalized...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
EuroJournals Publishing
2009
|
| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/12808/1/A%20recommender%20system%20approach%20for%20classifying%20user%20navigation%20patterns%20using%20longest%20common%20subsequence%20algorithm.pdf |
| _version_ | 1825945114945519616 |
|---|---|
| author | Jalali, Mehrdad Mustapha, Norwati Sulaiman, Md. Nasir Mamat, Ali |
| author_facet | Jalali, Mehrdad Mustapha, Norwati Sulaiman, Md. Nasir Mamat, Ali |
| author_sort | Jalali, Mehrdad |
| collection | UPM |
| description | Prediction of user future movements and intentions based on the users’ clickstream data is a main challenging problem in Web based recommendation systems. Web usage mining based on the users’ clickstream data has become the subject of exhaustive research, as its potential for web based personalized services, predicting user near future intentions, adaptive Web sites and customer profiling is recognized. A variety of the recommender systems for online personalization through web usage mining have been proposed.
However, the quality of the recommendations in the current systems to predict users’ future intentions systems cannot still satisfy users in the particular huge web sites. In this paper, to provide online predicting effectively, we develop a model for online predicting through web usage mining system and propose a novel approach for classifying user navigation patterns to predict users’ future intentions. The approach is based on the using longest
common subsequence algorithm to classify current user activities to predict user next movement. We have tested our proposed model on the CTI datasets. The results indicate
that the approach can improve the quality of the system for the predictions. |
| first_indexed | 2024-03-06T07:26:28Z |
| format | Article |
| id | upm.eprints-12808 |
| institution | Universiti Putra Malaysia |
| language | English English |
| last_indexed | 2024-03-06T07:26:28Z |
| publishDate | 2009 |
| publisher | EuroJournals Publishing |
| record_format | dspace |
| spelling | upm.eprints-128082015-10-16T07:03:11Z http://psasir.upm.edu.my/id/eprint/12808/ A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm. Jalali, Mehrdad Mustapha, Norwati Sulaiman, Md. Nasir Mamat, Ali Prediction of user future movements and intentions based on the users’ clickstream data is a main challenging problem in Web based recommendation systems. Web usage mining based on the users’ clickstream data has become the subject of exhaustive research, as its potential for web based personalized services, predicting user near future intentions, adaptive Web sites and customer profiling is recognized. A variety of the recommender systems for online personalization through web usage mining have been proposed. However, the quality of the recommendations in the current systems to predict users’ future intentions systems cannot still satisfy users in the particular huge web sites. In this paper, to provide online predicting effectively, we develop a model for online predicting through web usage mining system and propose a novel approach for classifying user navigation patterns to predict users’ future intentions. The approach is based on the using longest common subsequence algorithm to classify current user activities to predict user next movement. We have tested our proposed model on the CTI datasets. The results indicate that the approach can improve the quality of the system for the predictions. EuroJournals Publishing 2009 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/12808/1/A%20recommender%20system%20approach%20for%20classifying%20user%20navigation%20patterns%20using%20longest%20common%20subsequence%20algorithm.pdf Jalali, Mehrdad and Mustapha, Norwati and Sulaiman, Md. Nasir and Mamat, Ali (2009) A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm. American Journal of Scientific Research (4). pp. 17-27. ISSN 1450-223X Computer algorithms. Web usage mining. Recommender systems (Information filtering). English |
| spellingShingle | Computer algorithms. Web usage mining. Recommender systems (Information filtering). Jalali, Mehrdad Mustapha, Norwati Sulaiman, Md. Nasir Mamat, Ali A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm. |
| title | A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm. |
| title_full | A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm. |
| title_fullStr | A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm. |
| title_full_unstemmed | A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm. |
| title_short | A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm. |
| title_sort | recommender system approach for classifying user navigation patterns using longest common subsequence algorithm |
| topic | Computer algorithms. Web usage mining. Recommender systems (Information filtering). |
| url | http://psasir.upm.edu.my/id/eprint/12808/1/A%20recommender%20system%20approach%20for%20classifying%20user%20navigation%20patterns%20using%20longest%20common%20subsequence%20algorithm.pdf |
| work_keys_str_mv | AT jalalimehrdad arecommendersystemapproachforclassifyingusernavigationpatternsusinglongestcommonsubsequencealgorithm AT mustaphanorwati arecommendersystemapproachforclassifyingusernavigationpatternsusinglongestcommonsubsequencealgorithm AT sulaimanmdnasir arecommendersystemapproachforclassifyingusernavigationpatternsusinglongestcommonsubsequencealgorithm AT mamatali arecommendersystemapproachforclassifyingusernavigationpatternsusinglongestcommonsubsequencealgorithm AT jalalimehrdad recommendersystemapproachforclassifyingusernavigationpatternsusinglongestcommonsubsequencealgorithm AT mustaphanorwati recommendersystemapproachforclassifyingusernavigationpatternsusinglongestcommonsubsequencealgorithm AT sulaimanmdnasir recommendersystemapproachforclassifyingusernavigationpatternsusinglongestcommonsubsequencealgorithm AT mamatali recommendersystemapproachforclassifyingusernavigationpatternsusinglongestcommonsubsequencealgorithm |