A New Similarity Method to Optimize Business in the Online Stores Using the Rating Time Technology
These days, Emergence of e-commerce web sites is one of the important consequences of the Internet in modern times, but products data is growing exponentially. In such environment, customers face a problem in finding optimized information among huge data bases about the items or desired products. In...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Tehran
2017-03-01
|
| Series: | Journal of Information Technology Management |
| Subjects: | |
| Online Access: | https://jitm.ut.ac.ir/article_59844_8ae8b2cdc32145c1d0da84db072df70a.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850165571256057856 |
|---|---|
| author | nayereh zaghari Ardeshir Zamani |
| author_facet | nayereh zaghari Ardeshir Zamani |
| author_sort | nayereh zaghari |
| collection | DOAJ |
| description | These days, Emergence of e-commerce web sites is one of the important consequences of the Internet in modern times, but products data is growing exponentially. In such environment, customers face a problem in finding optimized information among huge data bases about the items or desired products. In order to assist buyers, large e-commerce companies are planning to introduce their own recommender systems to help their customers in making a better choice among the items. Due to high percentage error , a basic method to build such systems is not usually being applied. In this essay, two methods have been suggested in order to improve recommendations in recommender systems. Collaborative filtering method is one of the most successful methods used in the system, but using this method that it has common problem the increasing number of users and products, therefore system do not inability to request the requirement of cold start and data sparsity. Two methods have been suggested in order to improve recommendations in recommender systems. To resolve this problem, a new method has been introduced in which by integrating rating time by Pearson also combining semantic technology with social networks offers a solution to reduce issues such as "cold start" and generally "data sparsity" in recommender systems. The result of simulating showed that the proposed approach provided better performance and more accurate predictions in addition of more consistent with user preferences. |
| format | Article |
| id | doaj-art-ee2c94ae75c74a278ff5503e46da4ebe |
| institution | OA Journals |
| issn | 2008-5893 2423-5059 |
| language | English |
| publishDate | 2017-03-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj-art-ee2c94ae75c74a278ff5503e46da4ebe2025-08-20T02:21:42ZengUniversity of TehranJournal of Information Technology Management2008-58932423-50592017-03-0191618210.22059/jitm.2017.5984459844A New Similarity Method to Optimize Business in the Online Stores Using the Rating Time Technologynayereh zaghari0Ardeshir Zamani1Ph.D. Candidate in Computer Engineering, Azad University, Tehran, IranPh.D. Candidate in Business Management, Tehran University, Tehran, IranThese days, Emergence of e-commerce web sites is one of the important consequences of the Internet in modern times, but products data is growing exponentially. In such environment, customers face a problem in finding optimized information among huge data bases about the items or desired products. In order to assist buyers, large e-commerce companies are planning to introduce their own recommender systems to help their customers in making a better choice among the items. Due to high percentage error , a basic method to build such systems is not usually being applied. In this essay, two methods have been suggested in order to improve recommendations in recommender systems. Collaborative filtering method is one of the most successful methods used in the system, but using this method that it has common problem the increasing number of users and products, therefore system do not inability to request the requirement of cold start and data sparsity. Two methods have been suggested in order to improve recommendations in recommender systems. To resolve this problem, a new method has been introduced in which by integrating rating time by Pearson also combining semantic technology with social networks offers a solution to reduce issues such as "cold start" and generally "data sparsity" in recommender systems. The result of simulating showed that the proposed approach provided better performance and more accurate predictions in addition of more consistent with user preferences.https://jitm.ut.ac.ir/article_59844_8ae8b2cdc32145c1d0da84db072df70a.pdfData sparsityInternet storeRecommender systemsUsers rating time |
| spellingShingle | nayereh zaghari Ardeshir Zamani A New Similarity Method to Optimize Business in the Online Stores Using the Rating Time Technology Journal of Information Technology Management Data sparsity Internet store Recommender systems Users rating time |
| title | A New Similarity Method to Optimize Business
in the Online Stores Using the Rating
Time Technology |
| title_full | A New Similarity Method to Optimize Business
in the Online Stores Using the Rating
Time Technology |
| title_fullStr | A New Similarity Method to Optimize Business
in the Online Stores Using the Rating
Time Technology |
| title_full_unstemmed | A New Similarity Method to Optimize Business
in the Online Stores Using the Rating
Time Technology |
| title_short | A New Similarity Method to Optimize Business
in the Online Stores Using the Rating
Time Technology |
| title_sort | new similarity method to optimize business in the online stores using the rating time technology |
| topic | Data sparsity Internet store Recommender systems Users rating time |
| url | https://jitm.ut.ac.ir/article_59844_8ae8b2cdc32145c1d0da84db072df70a.pdf |
| work_keys_str_mv | AT nayerehzaghari anewsimilaritymethodtooptimizebusinessintheonlinestoresusingtheratingtimetechnology AT ardeshirzamani anewsimilaritymethodtooptimizebusinessintheonlinestoresusingtheratingtimetechnology AT nayerehzaghari newsimilaritymethodtooptimizebusinessintheonlinestoresusingtheratingtimetechnology AT ardeshirzamani newsimilaritymethodtooptimizebusinessintheonlinestoresusingtheratingtimetechnology |