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Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
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In the past decades, recommender systems have attracted much attention in both research and industry communities, and a large number of studies have been devoted to developing effective recommendation models. Basically speaking, these models mainly learn the underlying user preference from historical behavior data, and then estimate the user-item matching relationships for recommendations. Inspired by the recent progress on large language models (LLMs), we take a different approach to developing the recommendation models, considering recommendation as instruction following by LLMs. The key idea is that the preferences or needs of a user can be expressed in natural language descriptions (called instructions), so that LLMs can understand and further execute the instruction for fulfilling the recommendation task. Instead of using public APIs of LLMs, we instruction tune an open-source LLM (3B Flan-T5-XL), in order to better adapt LLMs to recommender systems. For this purpose, we first design a general instruction format for describing the preference, intention, task form and context of a user in natural language. Then we manually design 39 instruction templates and automatically generate a large amount of user-personalized instruction data (252K instructions) with varying types of preferences and intentions. To demonstrate the effectiveness of our approach, we instantiate the instruction templates into several widely-studied recommendation (or search) tasks, and conduct extensive experiments on these tasks with real-world datasets. Experiment results show that the proposed approach can outperform several competitive baselines, including the powerful GPT-3.5, on these evaluation tasks. Our approach sheds light on developing more user-friendly recommender systems, in which users can freely communicate with the system and obtain more accurate recommendations via natural language instructions.
Forward citations
Cited by 8 Pith papers
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LLM4MEA: Data-free Model Extraction Attacks on Sequential Recommenders via Large Language Models
An LLM-driven agent generates synthetic interaction sequences that, when queried against a target sequential recommender, produce surrogate models with higher agreement to the target than random or autoregressive data...
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A multi-grained reward framework fine-tunes an LLM with PPO to generate whole-page recommendations, showing that page-level and item-level reward heads are complementary.
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DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation
An LLM trained by reinforcement learning to interact over multiple turns with a preference-aware recommender model outperforms both traditional and LLM-based baselines on sequential recommendation benchmarks.
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Alibaba's Pailitao-MMSearch combines discrete product codes with a continuous embedding so a vision-language model can generate and rank products end-to-end, reporting big but incompletely documented A/B gains.
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RecoWorld: Building Simulated Environments for Agentic Recommender Systems
A design proposal, not a tested system: a dual-view simulation loop in which an LLM-simulated user issues reflective instructions when about to disengage, and an instruction-following recommender adapts to maximize si...
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TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendation
TrackRec trains a small LLM to generate user-preference summaries and a validator to score them, alternating the training so each improves the other, and reports gains on public and industrial recommendation benchmarks.
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