Social Group Recommendation With TrAdaBoost
Bibliographic Data
| ID | 22108687 |
|---|---|
| Authors | Zhenhua Huang (0000-0001-5990-8425, Tongji University), Juan Ni (0000-0003-3103-7095), Ni Juan (South China Normal University), Juanjuan Yao (0009-0001-2253-5028, Shanghai University of International Business and Economics), Xin Xu (0000-0002-9972-8995, South China Normal University), Bo Zhang (0000-0003-0986-0018, Shanghai Normal University), Yunwen Chen (0000-0002-7493-5672, China Datang Corporation (China)), Naiyu Tan (China Mobile (China)), Chao Xue (0000-0002-9408-3876, China Mobile (China)) |
| Year | 2020 |
| Volume | 7 |
| Issue | 5 |
| Pages | 1278-1287 |
| Publication date | 2020-10-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2020.3009721 |
| OpenAlex | W3046743961 |
| Language | EN |
| Citations received | 4 |
| References cited | 33 |
In recent years, group recommendation has become a research hotspot and focus in online social network community. Currently, several deep-learning-based approaches are leveraged to learn preferences of groups for items and predict the next items in which groups may be interested. Yet, their recommendation performance is still unsatisfactory due to the sparse group-item interactions. In order to address this problem, in this article, we introduce an effective model, namely Social Group Recommendation model with TrAdaBoost (SGRTAB), to raise the performance of group recommendation in online social networks. The SGRTAB model includes two stages: data preprocessing (DP) and model optimization (MO). In DP, SGRTAB produces inputs for MO and implements three related tasks: extracting individual features, handling group data via GloVe, and utilizing user contribute ratings to their own groups, whereas in MO, SGRTAB implements group preference learning with the assistance of user preference learning based on the TrAdaBoost algorithm. Specifically, SGRTAB can effectively absorb the knowledge of user preferences into the process of group preference learning through the idea of transferring-ensemble learning. Moreover, extensive experiments on four real-world data sets indicate that the proposed SGRTAB model significantly outperforms the state-of-the-art baselines for social group recommendation
Data science · Focus group · Machine learning · Preference · Preference learning · Preprocessor · Recommender system · Social group · Advanced Graph Neural Networks · Computer Science · Expert finding and Q&A systems · Psychology · Recommender Systems and Techniques · Artificial Intelligence
| Citations per year | 1.33 |
|---|---|
| Citation span | 2023 - 2026 (4) |
| Highly cited | No |