Recommender Systems Based On Time and Trust Using Graph Based Community Detection
Recently, the Internet has played a significant and substantial role in people's lives. However, the content available in the global web environment should align with users' daily needs, providing them with useful and up-to-date information tailored to their tastes. In this context, recomm...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | fas |
| Published: |
Allameh Tabataba'i University Press
2023-10-01
|
| Series: | مطالعات مدیریت کسب و کار هوشمند |
| Subjects: | |
| Online Access: | https://ims.atu.ac.ir/article_16692_d41d8cd98f00b204e9800998ecf8427e.pdf |
| _version_ | 1797386048567771136 |
|---|---|
| author | Fatemeh Rezaimehr Chitra Dadkhah |
| author_facet | Fatemeh Rezaimehr Chitra Dadkhah |
| author_sort | Fatemeh Rezaimehr |
| collection | DOAJ |
| description | Recently, the Internet has played a significant and substantial role in people's lives. However, the content available in the global web environment should align with users' daily needs, providing them with useful and up-to-date information tailored to their tastes. In this context, recommender systems assist users by suggesting items that closely match their preferences in less time. Today, with the exponential growth of data, the utilization of recommender systems has surged. Conversely, these systems encounter challenges such as evolving user preferences over time, cold start problem, sparsity within the user-item matrix, the infiltration of fake users in the systems, and their adverse impact on the recommendation lists.The objective of this paper is to propose a recommender system grounded in time and trust factors to enhance the efficiency and precision of system recommendations. Initially, the proposed system addresses the data sparsity dilemma by incorporating reliable implicit ratings into the user-item matrix. Subsequently, it constructs a weighted user-user network based on user rating timestamps and trust relationships among users, thereby mitigating the cold start problem and accounting for changing user preferences over time. The proposed recommender system employs a novel community detection algorithm introduced in this paper to identify the nearest neighbors of active users and recommends the top @k items based on the collaborative filtering approach. Evaluation results of the proposed system, tested on a film recommender system using the Epinions dataset, demonstrate its superior efficiency compared to basic systems. |
| first_indexed | 2024-03-08T22:03:15Z |
| format | Article |
| id | doaj.art-6469f0386faa4e23824ff73433927c67 |
| institution | Directory Open Access Journal |
| issn | 2821-0964 2821-0816 |
| language | fas |
| last_indexed | 2024-03-08T22:03:15Z |
| publishDate | 2023-10-01 |
| publisher | Allameh Tabataba'i University Press |
| record_format | Article |
| series | مطالعات مدیریت کسب و کار هوشمند |
| spelling | doaj.art-6469f0386faa4e23824ff73433927c672023-12-19T10:35:16ZfasAllameh Tabataba'i University Pressمطالعات مدیریت کسب و کار هوشمند2821-09642821-08162023-10-01124610.22054/ims.2023.72040.228216692Recommender Systems Based On Time and Trust Using Graph Based Community DetectionFatemeh Rezaimehr0Chitra Dadkhah1Computer Engineering Faculty, K. N. Toosi University of Technology-Recently, the Internet has played a significant and substantial role in people's lives. However, the content available in the global web environment should align with users' daily needs, providing them with useful and up-to-date information tailored to their tastes. In this context, recommender systems assist users by suggesting items that closely match their preferences in less time. Today, with the exponential growth of data, the utilization of recommender systems has surged. Conversely, these systems encounter challenges such as evolving user preferences over time, cold start problem, sparsity within the user-item matrix, the infiltration of fake users in the systems, and their adverse impact on the recommendation lists.The objective of this paper is to propose a recommender system grounded in time and trust factors to enhance the efficiency and precision of system recommendations. Initially, the proposed system addresses the data sparsity dilemma by incorporating reliable implicit ratings into the user-item matrix. Subsequently, it constructs a weighted user-user network based on user rating timestamps and trust relationships among users, thereby mitigating the cold start problem and accounting for changing user preferences over time. The proposed recommender system employs a novel community detection algorithm introduced in this paper to identify the nearest neighbors of active users and recommends the top @k items based on the collaborative filtering approach. Evaluation results of the proposed system, tested on a film recommender system using the Epinions dataset, demonstrate its superior efficiency compared to basic systems.https://ims.atu.ac.ir/article_16692_d41d8cd98f00b204e9800998ecf8427e.pdfrecommender systemstimetrustcommunity detectioncollaborative filtering |
| spellingShingle | Fatemeh Rezaimehr Chitra Dadkhah Recommender Systems Based On Time and Trust Using Graph Based Community Detection مطالعات مدیریت کسب و کار هوشمند recommender systems time trust community detection collaborative filtering |
| title | Recommender Systems Based On Time and Trust Using Graph Based Community Detection |
| title_full | Recommender Systems Based On Time and Trust Using Graph Based Community Detection |
| title_fullStr | Recommender Systems Based On Time and Trust Using Graph Based Community Detection |
| title_full_unstemmed | Recommender Systems Based On Time and Trust Using Graph Based Community Detection |
| title_short | Recommender Systems Based On Time and Trust Using Graph Based Community Detection |
| title_sort | recommender systems based on time and trust using graph based community detection |
| topic | recommender systems time trust community detection collaborative filtering |
| url | https://ims.atu.ac.ir/article_16692_d41d8cd98f00b204e9800998ecf8427e.pdf |
| work_keys_str_mv | AT fatemehrezaimehr recommendersystemsbasedontimeandtrustusinggraphbasedcommunitydetection AT chitradadkhah recommendersystemsbasedontimeandtrustusinggraphbasedcommunitydetection |