1,747 research outputs found
RsyGAN: Generative Adversarial Network for Recommender Systems
© 2019 IEEE. Many recommender systems rely on the information of user-item interactions to generate recommendations. In real applications, the interaction matrix is usually very sparse, as a result, the model cannot be optimised stably with different initial parameters and the recommendation performance is unsatisfactory. Many works attempted to solve this problem, however, the parameters in their models may not be trained effectively due to the sparse nature of the dataset which results in a lower quality local optimum. In this paper, we propose a generative network for making user recommendations and a discriminative network to guide the training process. An adversarial training strategy is also applied to train the model. Under the guidance of a discriminative network, the generative network converges to an optimal solution and achieves better recommendation performance on a sparse dataset. We also show that the proposed method significantly improves the precision of the recommendation performance on several datasets
LLM-Rec: Personalized Recommendation via Prompting Large Language Models
We investigate various prompting strategies for enhancing personalized
recommendation performance with large language models (LLMs) through input
augmentation. Our proposed approach, termed LLM-Rec, encompasses four distinct
prompting strategies: (1) basic prompting, (2) recommendation-driven prompting,
(3) engagement-guided prompting, and (4) recommendation-driven +
engagement-guided prompting. Our empirical experiments show that incorporating
the augmented input text generated by LLM leads to improved recommendation
performance. Recommendation-driven and engagement-guided prompting strategies
are found to elicit LLM's understanding of global and local item
characteristics. This finding highlights the importance of leveraging diverse
prompts and input augmentation techniques to enhance the recommendation
capabilities with LLMs
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