Recommendation Model
Recommendation models aim to predict user preferences and provide personalized suggestions, primarily focusing on improving accuracy, efficiency, and fairness. Current research emphasizes addressing challenges like data sparsity, cold starts, popularity bias, and the impact of noisy data through techniques such as multi-modal approaches, knowledge graph integration, attention mechanisms, and self-supervised learning. These advancements have significant implications for various applications, including e-commerce, entertainment, and even healthcare, by enhancing user experience and enabling more effective information filtering.
Papers
Long-Sequence Recommendation Models Need Decoupled Embeddings
Ningya Feng, Junwei Pan, Jialong Wu, Baixu Chen, Ximei Wang, Qian Li, Xian Hu, Jie Jiang, Mingsheng Long
Multi-modal clothing recommendation model based on large model and VAE enhancement
Bingjie Huang, Qingyi Lu, Shuaishuai Huang, Xue-she Wang, Haowei Yang
Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method
Chen Yang, Sunhao Dai, Yupeng Hou, Wayne Xin Zhao, Jun Xu, Yang Song, Hengshu Zhu
Data-driven Conditional Instrumental Variables for Debiasing Recommender Systems
Zhirong Huang, Shichao Zhang, Debo Cheng, Jiuyong Li, Lin Liu, Guangquan Lu