Article

User Preference Translation Model for Next Top-k Items Recommendation with Social Relations

Authors:

Database Systems for Advanced Applications: 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11–14, 2021, Proceedings, Part III

Pages 652 - 655

https://doi.org/10.1007/978-3-030-73200-4_54

Published: 11 April 2021 Publication History

Abstract

Recommendation systems are used to predict the interests of users through the analysis of historical preferences. Collaborative filtering-based approaches usually ignore the sequential information and sequential recommendation usually focus on the next item prediction. In this work, we would like to determine the next top-k recommendation problem. We propose User Preference Translation Model (UPTM) with item influence embedding and social relations between users. In addition, we will also solve the cold start problem in UPTM.

References

[1]

Chae, D.-K., Kang, J., Kang, J.-S., Kim, S.-W., Lee, J., Lee, J.-T.: CFGAN: a generic collaborative filtering framework based on generative adversarial networks. In: Proceedings of the 27th ACM CIKM, pp. 137–146 (2018)

Google Scholar

[2]

He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of WWW, pp. 173–182 (2017)

Google Scholar

[3]

Ma, H.-S., Huang, J.-W.: User preference translation model for recommendation system with item influence diffusion embedding. In: Proceedings of IEEE/ACM International Conference on ASONAM, pp. 50–54 (2020)

Google Scholar

[4]

Wang, X., He, X., Wang, M., Feng, F., Chua, T.-S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR, pp. 165–174 (2019)

Google Scholar

[5]

Wu, L., Sun, P., Fu, Y., Hong, R., Wang, X., Wang, M.: A neural influence diffusion model for social recommendation. In: Proceedings of the 42nd International ACM SIGIR, pp. 235–244 (2019)

Google Scholar

[6]

Zhu T, Liu G, and Chen G Social collaborative mutual learning for item recommendation ACM Trans. Knowl. Discov. Data 2020 14 1-19

Google Scholar

Index Terms

User Preference Translation Model for Next Top-k Items Recommendation with Social Relations
1. Computing methodologies
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

Recommendations

User-Specific Feature-Based Similarity Models for Top-n Recommendation of New Items
Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence

Recommending new items for suitable users is an important yet challenging problem due to the lack of preference history for the new items. Noncollaborative user modeling techniques that rely on the item features can be used to recommend new items. ...
Embedding Factorization Models for Jointly Recommending Items and User Generated Lists
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Existing recommender algorithms mainly focused on recommending individual items by utilizing user-item interactions. However, little attention has been paid to recommend user generated lists (e.g., playlists and booklists). On one hand, user generated ...
Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings

Prior research has shown a robust effect of personalized product recommendations on user preference judgments for items. Specifically, the display of system-predicted preference ratings as item recommendations has been shown in multiple studies to bias ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

Database Systems for Advanced Applications: 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11–14, 2021, Proceedings, Part III

Apr 2021

691 pages

ISBN:978-3-030-73199-1

DOI:10.1007/978-3-030-73200-4

Editors:
Christian S. Jensen
Aalborg University, Aalborg, Denmark
,
Ee-Peng Lim
Singapore Management University, Singapore, Singapore
,
De-Nian Yang
Academia Sinica, Taipei, Taiwan
,
Wang-Chien Lee
The Pennsylvania State University, University Park, PA, USA
,
Vincent S. Tseng
National Chiao Tung University, Hsinchu, Taiwan
,
Vana Kalogeraki
Athens University of Economics and Business, Athens, Greece
,
Jen-Wei Huang
National Cheng Kung University, Tainan City, Taiwan
,
Chih-Ya Shen
National Tsing Hua University, Hsinchu, Taiwan

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 11 April 2021

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Sep 2024

Other Metrics

View Author Metrics

Citations

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Index Terms

Recommendations

User-Specific Feature-Based Similarity Models for Top-n Recommendation of New Items

Embedding Factorization Models for Jointly Recommending Items and User Generated Lists

Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

View options

Get Access

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations