User Knowledge Prompt for Sequential Recommendation

The large language model (LLM) based recommendation system is effective for sequential recommendation, because general knowledge of popular items is included in the LLM. To add domain knowledge of items, the conventional method uses a knowledge prompt obtained from the item knowledge graphs and has achieved SOTA performance. However, for personalized recommendation, it is necessary to consider user knowledge, which the conventional method does not fully consider because user knowledge is not included in the item knowledge graphs; thus, we propose a user knowledge prompt, which converts a user knowledge graph into a prompt using the relationship template. The existing prompt denoising framework is extended to prevent hallucination caused by undesirable interactions between knowledge graph prompts. We propose user knowledge prompts of user traits and user preferences and associate relevant items. Experiments on three types of dataset (movie, music, and book) show the significant and consistent improvement of our proposed user knowledge prompt.