  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.
