Abstract
Matrix factorization (MF) is a popular method for collaborative filtering. Recently, more and more MF methods have been proposed to incorporate side information. However, most of them are vulnerable to changes in data or sub-models. Moreover, data often follows a Pareto distribution and such an imbalance of data leads to a biased global mean, affecting the prediction accuracy. To overcome these defects, we designed a Hierarchical Generalized Linear Model-based MF method (HGLMMF) which can leverage both the original and processed side information. More specifically, HGLMMF utilizes one portion of the side information to construct covariates for fixed effects and the other portion to model the cluster-specific effects to adjust the global-bias problem. In fact, a number of state-of-the-art MF models can be viewed as special cases of HGLMMF. The obtained prediction results from experiments prove that HGLMMF is highly competitive with state-of-the-art methods.
Original language | English |
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Title of host publication | 2022 IEEE 9th International Conference on Data Science and Advanced Analytics DSAA'2022 |
Subtitle of host publication | proceedings |
Editors | Joshua Zhexue Huang, Yi Pan, Barbara Hammer, Muhammad Khurram Khan, Xing Xie, Laizhong Cui, Yulin He |
Place of Publication | Piscataway, NJ |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Number of pages | 9 |
ISBN (Electronic) | 9781665473309 |
ISBN (Print) | 9781665473316 |
DOIs | |
Publication status | Published - 2022 |
Externally published | Yes |
Event | 9th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2022 - Shenzhen, China Duration: 13 Oct 2022 → 16 Oct 2022 |
Conference
Conference | 9th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2022 |
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Country/Territory | China |
City | Shenzhen |
Period | 13/10/22 → 16/10/22 |
Keywords
- matrix factorization
- hierarchical generalized linear mixed model
- side information
- cold start
- recommender systems