In this paper, we introduce PLUM, a framework designed to adapt pre-trained LLMs for industry-scale recommendation tasks. PLUM consists of item tokenization using Semantic IDs, continued pre-training (CPT) on domain-specific data, and task-specific fine-tuning for recommendation objectives. We conduct comprehensive experiments on large-scale internal video recommendation datasets and demonstrate substantial improvements for retrieval compared to a heavily-optimized production model.
This article explores NDCG — Normalized Discounted Cumulative Gain, a rank-aware metric for evaluating recommendation system models.
The article addresses the challenges in recommendation systems, specifically dealing with new users and items (cold-start problem), and the computational inefficiency and scalability issues of traditional embedding-based models.
ByteDance introduced the Hierarchical Large Language Model (HLLM), designed to improve sequential recommendations. The HLLM consists of two components: an Item LLM and a User LLM. The Item LLM extracts detailed features from item descriptions and generates embeddings that are then processed by the User LLM to predict user behavior. This hierarchical approach allows for efficient and effective handling of new items and users.
ByteDance introduced the Hierarchical Large Language Model (HLLM), designed to improve sequential recommendations. The HLLM consists of two components: an Item LLM and a User LLM. The Item LLM extracts detailed features from item descriptions and generates embeddings that are then processed by the User LLM to predict user behavior. This hierarchical approach allows for efficient and effective handling of new items and users.
