Adaptive social recommendation combined with the multi-domain influence. Issue 113 (January 2023)
- Record Type:
- Journal Article
- Title:
- Adaptive social recommendation combined with the multi-domain influence. Issue 113 (January 2023)
- Main Title:
- Adaptive social recommendation combined with the multi-domain influence
- Authors:
- Qian, Fulan
Qin, Kaili
Chen, Hai
Chen, Jie
Zhao, Shu
Zhang, Yanping - Abstract:
- Abstract: Social relationships help to model user's potential preferences and improve recommendation accuracy. In social recommendation, user decision-making will be affected by his own historical interaction items and social friends. Most social recommendations consider these two aspects separately, but users are also driven by both when making decisions. To address the above issue, this paper proposes an adaptive social recommendation method based on attention mechanism, Adaptive Social Recommendation combine with Multi-domain influence (ASRM). Specifically, we defined three domains, item domain, social domain, and common domain to measure the possible impact of social relations and item interactions on users. We propose an adaptive attention module to simulate users being influenced by social relations and historical interaction items in these three domains, and obtain user's potential preferences. In addition, we jointly optimize by calculating the loss of each domain, resulting in more accurate recommendations. A large number of experiments on three real datasets have demonstrated the effectiveness of our method. The code is available at https://github.com/qinkaili/ASRM . Highlights: A new multi-domain adaptive social recommendation method. Considers the combined impact of historical items and social information. Item, social, and common domain are used to simulate item and social impact on users. Using attention mechanism to distinguish the importance of domainAbstract: Social relationships help to model user's potential preferences and improve recommendation accuracy. In social recommendation, user decision-making will be affected by his own historical interaction items and social friends. Most social recommendations consider these two aspects separately, but users are also driven by both when making decisions. To address the above issue, this paper proposes an adaptive social recommendation method based on attention mechanism, Adaptive Social Recommendation combine with Multi-domain influence (ASRM). Specifically, we defined three domains, item domain, social domain, and common domain to measure the possible impact of social relations and item interactions on users. We propose an adaptive attention module to simulate users being influenced by social relations and historical interaction items in these three domains, and obtain user's potential preferences. In addition, we jointly optimize by calculating the loss of each domain, resulting in more accurate recommendations. A large number of experiments on three real datasets have demonstrated the effectiveness of our method. The code is available at https://github.com/qinkaili/ASRM . Highlights: A new multi-domain adaptive social recommendation method. Considers the combined impact of historical items and social information. Item, social, and common domain are used to simulate item and social impact on users. Using attention mechanism to distinguish the importance of domain information. … (more)
- Is Part Of:
- Information systems. Issue 113(2023)
- Journal:
- Information systems
- Issue:
- Issue 113(2023)
- Issue Display:
- Volume 113, Issue 113 (2023)
- Year:
- 2023
- Volume:
- 113
- Issue:
- 113
- Issue Sort Value:
- 2023-0113-0113-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-01
- Subjects:
- Recommender system -- Social influence -- Attention mechanism
Database management -- Periodicals
Electronic data processing -- Periodicals
Bases de données -- Gestion -- Périodiques
Informatique -- Périodiques
Database management
Electronic data processing
Periodicals
005.7 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03064379 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.is.2022.102145 ↗
- Languages:
- English
- ISSNs:
- 0306-4379
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4496.367300
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25942.xml