In the realm of personalized recommender systems, the challenge of adapting
to evolving user preferences and the continuous influx of new users and items
is paramount. Conventional models, typically reliant on a static training-test
approach, struggle to keep pace with these dynamic demands. Streaming
recommendation, particularly through continual graph learning, has emerged as a
novel solution. However, existing methods in this area either rely on
historical data replay, which is increasingly impractical due to stringent data
privacy regulations; or are inability to effectively address the over-stability
issue; or depend on model-isolation and expansion strategies. To tackle these
difficulties, we present GPT4Rec, a Graph Prompt Tuning method for streaming
Recommendation. Given the evolving user-item interaction graph, GPT4Rec first
disentangles the graph patterns into multiple views. After isolating specific
interaction patterns and relationships in different views, GPT4Rec utilizes
lightweight graph prompts to efficiently guide the model across varying
interaction patterns within the user-item graph. Firstly, node-level prompts
are employed to instruct the model to adapt to changes in the attributes or
properties of individual nodes within the graph. Secondly, structure-level
prompts guide the model in adapting to broader patterns of connectivity and
relationships within the graph. Finally, view-level prompts are innovatively
designed to facilitate the aggregation of information from multiple
disentangled views. These prompt designs allow GPT4Rec to synthesize a
comprehensive understanding of the graph, ensuring that all vital aspects of
the user-item interactions are considered and effectively integrated.
Experiments on four diverse real-world datasets demonstrate the effectiveness
and efficiency of our proposal.

GPT4Rec 是一种用于流式推荐的图形提示调整方法，通过将图形模式解开为多个视图，并利用轻量级图形提示来引导模型适应用户 - 项目图中不同的交互模式，以综合理解图形并有效集成用户 - 项目交互的所有重要方面。

GPT4Rec: 图形提示调整的流媒体推荐

GPT4Rec: Graph Prompt Tuning for Streaming Recommendation

Recent advancements in Natural Language Processing (NLP) have led to the
development of NLP-based recommender systems that have shown superior
performance. However, current models commonly treat items as mere IDs and adopt
discriminative modeling, resulting in limitations of (1) fully leveraging the
content information of items and the language modeling capabilities of NLP
models; (2) interpreting user interests to improve relevance and diversity; and
(3) adapting practical circumstances such as growing item inventories. To
address these limitations, we present GPT4Rec, a novel and flexible generative
framework inspired by search engines. It first generates hypothetical "search
queries" given item titles in a user's history, and then retrieves items for
recommendation by searching these queries. The framework overcomes previous
limitations by learning both user and item embeddings in the language space. To
well-capture user interests with different aspects and granularity for
improving relevance and diversity, we propose a multi-query generation
technique with beam search. The generated queries naturally serve as
interpretable representations of user interests and can be searched to
recommend cold-start items. With GPT-2 language model and BM25 search engine,
our framework outperforms state-of-the-art methods by $75.7\%$ and $22.2\%$ in
Recall@K on two public datasets. Experiments further revealed that multi-query
generation with beam search improves both the diversity of retrieved items and
the coverage of a user's multi-interests. The adaptiveness and interpretability
of generated queries are discussed with qualitative case studies.

本文介绍了 GPT4Rec，一种基于生成模型和搜索引擎的新型灵活推荐系统，它通过多查询生成技术和基于 BM25 的搜索引擎来更好地捕获用户兴趣，提高推荐的相关性和多样性。