A Novel Group Recommendation Mechanism From the Perspective of Preference Distribution
As people have become more and more connected, there are certain scenarios where items need to be recommended to groups of users rather than individual user, which motivate studies on group recommender systems (GRSs). However, developing GRSs is not an easy task, because a group consists of multiple...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8255564/ |
| _version_ | 1819169228911017984 |
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| author | Zhiwei Guo Chaowei Tang Hui Tang Yunqing Fu Wenjia Niu |
| author_facet | Zhiwei Guo Chaowei Tang Hui Tang Yunqing Fu Wenjia Niu |
| author_sort | Zhiwei Guo |
| collection | DOAJ |
| description | As people have become more and more connected, there are certain scenarios where items need to be recommended to groups of users rather than individual user, which motivate studies on group recommender systems (GRSs). However, developing GRSs is not an easy task, because a group consists of multiple members with heterogeneous preferences. How to make a trade-off among their preferences remains challenging. Existing works almost aggregate members' preferences into forms of single values as group profile. However, simple aggregations fail to well reflect comprehensive group profile when it comes to groups with highly conflicting preferences. In this paper, we propose Greption, a novel group recommendation mechanism from the perspective of preference distribution. First, based on preference distributions toward items in training set, a multi-dimensional support vector regression model is established to predict preference distributions toward candidate items. Then, through a modified VIKOR method, we transform the process of selecting items for a group into a multi-criteria decision making process. Furthermore, the Greption is extended to be able to handle data sparsity. Specifically, we propose two heuristic schemes for this purpose. And we present a set of experiments to evaluate the efficiency of the Greption. |
| first_indexed | 2024-12-22T19:16:10Z |
| format | Article |
| id | doaj.art-0a3d81598c7b457f962645616f4a2783 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:16:10Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0a3d81598c7b457f962645616f4a27832022-12-21T18:15:31ZengIEEEIEEE Access2169-35362018-01-0165865587810.1109/ACCESS.2018.27924278255564A Novel Group Recommendation Mechanism From the Perspective of Preference DistributionZhiwei Guo0https://orcid.org/0000-0001-8868-6913Chaowei Tang1Hui Tang2Yunqing Fu3Wenjia Niu4College of Communication Engineering, Chongqing University, Chongqing, ChinaCollege of Communication Engineering, Chongqing University, Chongqing, ChinaCollege of Communication Engineering, Chongqing University, Chongqing, ChinaCollege of Software, Chongqing University, Chongqing, ChinaBeijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, ChinaAs people have become more and more connected, there are certain scenarios where items need to be recommended to groups of users rather than individual user, which motivate studies on group recommender systems (GRSs). However, developing GRSs is not an easy task, because a group consists of multiple members with heterogeneous preferences. How to make a trade-off among their preferences remains challenging. Existing works almost aggregate members' preferences into forms of single values as group profile. However, simple aggregations fail to well reflect comprehensive group profile when it comes to groups with highly conflicting preferences. In this paper, we propose Greption, a novel group recommendation mechanism from the perspective of preference distribution. First, based on preference distributions toward items in training set, a multi-dimensional support vector regression model is established to predict preference distributions toward candidate items. Then, through a modified VIKOR method, we transform the process of selecting items for a group into a multi-criteria decision making process. Furthermore, the Greption is extended to be able to handle data sparsity. Specifically, we propose two heuristic schemes for this purpose. And we present a set of experiments to evaluate the efficiency of the Greption.https://ieeexplore.ieee.org/document/8255564/Group recommendation mechanismpreference distributionmulti-criteria decision makingcollaborative filtering |
| spellingShingle | Zhiwei Guo Chaowei Tang Hui Tang Yunqing Fu Wenjia Niu A Novel Group Recommendation Mechanism From the Perspective of Preference Distribution IEEE Access Group recommendation mechanism preference distribution multi-criteria decision making collaborative filtering |
| title | A Novel Group Recommendation Mechanism From the Perspective of Preference Distribution |
| title_full | A Novel Group Recommendation Mechanism From the Perspective of Preference Distribution |
| title_fullStr | A Novel Group Recommendation Mechanism From the Perspective of Preference Distribution |
| title_full_unstemmed | A Novel Group Recommendation Mechanism From the Perspective of Preference Distribution |
| title_short | A Novel Group Recommendation Mechanism From the Perspective of Preference Distribution |
| title_sort | novel group recommendation mechanism from the perspective of preference distribution |
| topic | Group recommendation mechanism preference distribution multi-criteria decision making collaborative filtering |
| url | https://ieeexplore.ieee.org/document/8255564/ |
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