An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group Preferences
Group preferences are crucial for Inspirational Solutions of Automotive Design (ISAD). However, sparse individual purchase behavior hinders the identification of group preferences. Therefore, a novel inspiration recommendation (IR) system based on multi-level mining of user behavior data is proposed...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/12/11/491 |
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| author | Wanxin Cai Mingqing Yang Li Lin |
| author_facet | Wanxin Cai Mingqing Yang Li Lin |
| author_sort | Wanxin Cai |
| collection | DOAJ |
| description | Group preferences are crucial for Inspirational Solutions of Automotive Design (ISAD). However, sparse individual purchase behavior hinders the identification of group preferences. Therefore, a novel inspiration recommendation (IR) system based on multi-level mining of user behavior data is proposed. Firstly, the K-means algorithm is employed to cluster users based on a variety of features. The fixed association rule is then applied to filter and identify relevant subsets, forming the foundational basis for constructing a user portrait. The Nonlinear Bayesian Personalized Ranking (NBPR) is constructed to explore common preferences using explicit feedback. Finally, the item preference matrix is enriched with implicit feedback to compile a comprehensive recommendation list that caters to group preferences. Using a multi-user joint evaluation approach, we compare the performance of IR with baseline models across multiple metrics. This comparison demonstrates the robust reliability of the IR system and its ability to prioritize ISAD with preference-aligned groups. Our research overcomes data sparsity in the automotive recommendation system, providing a new method for embedding human elements in decision support systems. |
| format | Article |
| id | doaj-art-1d1572c98c6745ed815d1ec1e4f11168 |
| institution | Kabale University |
| issn | 2079-8954 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-1d1572c98c6745ed815d1ec1e4f111682024-11-26T18:23:24ZengMDPI AGSystems2079-89542024-11-01121149110.3390/systems12110491An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group PreferencesWanxin Cai0Mingqing Yang1Li Lin2School of Mechanical Engineering, Guizhou University, Guiyang 550000, ChinaSchool of Mechanical Engineering, Guizhou University, Guiyang 550000, ChinaSchool of Mechanical Engineering, Guizhou University, Guiyang 550000, ChinaGroup preferences are crucial for Inspirational Solutions of Automotive Design (ISAD). However, sparse individual purchase behavior hinders the identification of group preferences. Therefore, a novel inspiration recommendation (IR) system based on multi-level mining of user behavior data is proposed. Firstly, the K-means algorithm is employed to cluster users based on a variety of features. The fixed association rule is then applied to filter and identify relevant subsets, forming the foundational basis for constructing a user portrait. The Nonlinear Bayesian Personalized Ranking (NBPR) is constructed to explore common preferences using explicit feedback. Finally, the item preference matrix is enriched with implicit feedback to compile a comprehensive recommendation list that caters to group preferences. Using a multi-user joint evaluation approach, we compare the performance of IR with baseline models across multiple metrics. This comparison demonstrates the robust reliability of the IR system and its ability to prioritize ISAD with preference-aligned groups. Our research overcomes data sparsity in the automotive recommendation system, providing a new method for embedding human elements in decision support systems.https://www.mdpi.com/2079-8954/12/11/491user behavior datapreference-aligned groupsnonlinear Bayesian personalized rankinginspiration recommendation |
| spellingShingle | Wanxin Cai Mingqing Yang Li Lin An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group Preferences Systems user behavior data preference-aligned groups nonlinear Bayesian personalized ranking inspiration recommendation |
| title | An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group Preferences |
| title_full | An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group Preferences |
| title_fullStr | An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group Preferences |
| title_full_unstemmed | An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group Preferences |
| title_short | An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group Preferences |
| title_sort | inspiration recommendation system for automotive styling design based on user behavior data and group preferences |
| topic | user behavior data preference-aligned groups nonlinear Bayesian personalized ranking inspiration recommendation |
| url | https://www.mdpi.com/2079-8954/12/11/491 |
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