SetaCRS： Conversational recommender system with structure-enhanced hierarchical task-oriented prompting strategy

doi:10.11772/j.issn.1001-9081.2025020256

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (2): 368-377.DOI: 10.11772/j.issn.1001-9081.2025020256

• Artificial intelligence • Previous Articles

SetaCRS： Conversational recommender system with structure-enhanced hierarchical task-oriented prompting strategy

Haoqian JIANG¹, Dong ZHANG¹, Guanyu LI¹(), Heng CHEN²

^1.Information Sciences and Technology College，Dalian Maritime University，Dalian Liaoning 116026，China
^2.School for Software Engineering，Dalian University of Foreign Languages，Dalian Liaoning 116044，China

Received:2025-03-17 Revised:2025-05-20 Accepted:2025-05-28 Online:2025-06-10 Published:2026-02-10
Contact: Guanyu LI
About author:JIANG Haoqian， born in 2001， M. S. candidate. His research interests include intelligent information processing， recommender system.
ZHANG Dong， born in 1996， Ph. D. candidate. His research interests include natural language processing， knowledge graph.
LI Guanyu， born in 1963， Ph. D.， professor. His research interests include semantic web， intelligent information processing. Email:liguanyu@dlmu.edu.cn
CHEN Heng， born in 1982， Ph. D. His research interests include knowledge graph， knowledge graph completion.
Supported by:
National Natural Science Foundation of China(61976032);2024 Liaoning Provincial Natural Science Foundation （General Project）(2024-MS-174)

基于结构增强的层次化任务导向提示策略的对话推荐系统SetaCRS

姜皓骞¹, 张东¹, 李冠宇¹(), 陈恒²

^1.大连海事大学信息科学技术学院，辽宁大连 116026
^2.大连外国语大学软件学院，辽宁大连 116044

通讯作者: 李冠宇
作者简介:姜皓骞（2001—），男，辽宁锦州人，硕士研究生，CCF会员，主要研究方向：智能信息处理、推荐系统
张东（1996—），男，辽宁鞍山人，博士研究生，主要研究方向：自然语言处理、知识图谱
李冠宇（1963—），男，辽宁大连人，教授，博士，主要研究方向：语义网、智能信息处理 Email:liguanyu@dlmu.edu.cn
陈恒（1982—），男，辽宁大连人，博士，主要研究方向：知识图谱、知识图谱补全。
基金资助:
国家自然科学基金资助项目(61976032);2024年辽宁省自然科学基金资助项目（面上项目）(2024-MS-174)

Abstract

Abstract:

In recent years， many studies on conversational recommender systems use pre-trained language models as unified frameworks， aiming to address the issue of inadequate coordination among modules in traditional multi-module architecture. However， these methods cannot fully exploit the coordination between tasks and fail to capture potential structured information in the input effectively. These problems weaken the performance of conversational recommender systems in real-world application scenarios significantly. Therefore， a Conversational Recommender System with Structure-enhanced hierarchical task-oriented prompting strategy named SetaCRS was proposed. In SetaCRS， a heterogeneous graph attention neural network was used to model the sequence co-occurrence information in historical conversations between the user and the system. In addition， hierarchical global task descriptions and specific sub-task descriptions were constructed to help the model capture and utilize the relationship between the current sub-task and the overall task sequence. Experimental results on two public datasets， DuRecDial and TG-ReDial， show that compared with UniMIND （Unified MultI-goal conversational recommeNDer system）， SetaCRS achieves improvements of 8.53% and 1.55% in semantic F1， respectively， and improvements of 3.02% and 9.54% in Mean Reciprocal Rank （MRR）@10， respectively. It can be seen that the task dependencies and conversational structured information captured by SetaCRS improve recommendation accuracy and response quality effectively.

Key words: conversational recommender system, prompt engineering, pre-trained language model, graph neural network, deep learning

摘要：

近年来，许多对话推荐系统的研究采用预训练语言模型作为统一框架，旨在解决传统多模块架构中模块间协同不当的问题；然而，这些方法难以发挥任务之间的协同作用，且无法有效捕获输入中潜在的结构化信息，这些问题很大程度上削弱了对话推荐系统在实际应用场景中的表现。因此，提出一种基于结构增强的层次化任务导向提示策略的对话推荐系统SetaCRS。SetaCRS利用异质图注意力神经网络建模用户系统历史对话中的序列共现信息。此外，构造层次化的全局任务描述和特定子任务描述，从而帮助模型捕获并利用当前子任务和总任务序列之间的联系。在DuRecDial与TG-ReDial这2个公开数据集上的实验结果表明，相较于UniMIND（Unified MultI-goal conversational recommeNDer system），SetaCRS在语义F1上分别提升了8.53%和1.55%，并在平均倒数排名（MRR）@10上分别提升了3.02%和9.54%。可见，SetaCRS能够利用所捕捉的任务关联性与对话结构信息来有效提升推荐准确性和回复质量。

CLC Number:

TP391

Haoqian JIANG, Dong ZHANG, Guanyu LI, Heng CHEN. SetaCRS： Conversational recommender system with structure-enhanced hierarchical task-oriented prompting strategy[J]. Journal of Computer Applications, 2026, 46(2): 368-377.

姜皓骞, 张东, 李冠宇, 陈恒. 基于结构增强的层次化任务导向提示策略的对话推荐系统SetaCRS[J]. 《计算机应用》唯一官方网站, 2026, 46(2): 368-377.

Figures/Tables 11

References 59

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数据集	对话数量	对话轮次	对话目标	对话主题	候选物品
TG-ReDial	10 000	129 392	8	2 571	33 834
DuRecDial	10 190	155 477	21	701	11 162

数据集	对话数量	对话轮次	对话目标	对话主题	候选物品
TG-ReDial	10 000	129 392	8	2 571	33 834
DuRecDial	10 190	155 477	21	701	11 162

模型	NDCG@10	NDCG@50	MRR@10	MRR@50
SASRec	0.369	0.413	0.307	0.317
BERT	0.546	0.572	0.498	0.504
LLAMA-13b	0.027	0.031	0.024	0.024
Union	0.557	0.583	0.510	0.516
ChatGPT	0.024	0.035	0.018	0.020
GRU4Rec	0.219	0.273	0.171	0.183
TextCNN	0.505	0.534	0.452	0.458
UniMIND	0.634	0.647	0.629	0.632
SetaCRS	0.664	0.700	0.648	0.659

模型	NDCG@10	NDCG@50	MRR@10	MRR@50
SASRec	0.369	0.413	0.307	0.317
BERT	0.546	0.572	0.498	0.504
LLAMA-13b	0.027	0.031	0.024	0.024
Union	0.557	0.583	0.510	0.516
ChatGPT	0.024	0.035	0.018	0.020
GRU4Rec	0.219	0.273	0.171	0.183
TextCNN	0.505	0.534	0.452	0.458
UniMIND	0.634	0.647	0.629	0.632
SetaCRS	0.664	0.700	0.648	0.659

模型	NDCG@10	NDCG@50	MRR@10	MRR@50
SASRec	0.009 2	0.017 9	0.005 0	0.006 8
GRU4Rec	0.002 8	0.006 2	0.001 4	0.002 0
MHIM	0.015 2	0.025 6	0.010 8	0.012 9
BERT	0.024 6	0.043 9	0.018 2	0.021 1
LLMCRS（Flan-T5）	0.015 9	0.026 1	0.012 8	0.013 8
Union	0.034 8	0.052 7	0.024 0	0.027 7
UniMIND	0.038 6	0.063 8	0.028 3	0.031 9
TextCNN	0.014 4	0.021 5	0.011 9	0.013 3
SetaCRS	0.040 3	0.064 1	0.031 0	0.035 7

SetaCRS： Conversational recommender system with structure-enhanced hierarchical task-oriented prompting strategy

基于结构增强的层次化任务导向提示策略的对话推荐系统SetaCRS

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Figures/Tables 11

References 59

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Metrics

回复任务	F1/%	BLEU-1	BLEU2	Dist-2
UniMIND	52.19	0.479	0.398	0.079
TPNet	NA	0.308	0.217	0.093
MGCG	42.04	0.362	0.252	0.081
GPT-2	47.01	0.392	0.295	0.165
KERS	50.47	0.463	0.362	0.079
BART	48.41	0.418	0.328	0.049
GOKC	47.28	0.413	0.318	0.084
SetaCRS	53.00	0.506	0.426	0.086

回复任务	F1/%	BLEU-1	BLEU-2	Dist-2
UniMIND	35.62	0.314	0.090	0.198
GPT-2	NA	0.279	0.066	0.094
COLA	NA	NA	0.025	0.151
BART	32.80	0.291	0.070	0.097
Union	NA	0.280	0.065	0.094
SetaCRS	38.66	0.341	0.091	0.186

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