Recipe recommendation model based on hierarchical learning of flavor embedding heterogeneous graph

doi:10.11772/j.issn.1001-9081.2024060859

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (6): 1869-1878.DOI: 10.11772/j.issn.1001-9081.2024060859

• Data science and technology • Previous Articles

Recipe recommendation model based on hierarchical learning of flavor embedding heterogeneous graph

Wenjing YAN¹^,², Ruidong WANG¹^,², Min ZUO¹^,³(), Qingchuan ZHANG¹^,²

^1.National Engineering Research Centre for Agri-product Quality Traceability，Beijing Technology and Business University，Beijing 100048，China
^2.School of Computer and Artificial Intelligence，Beijing Technology and Business University，Beijing 100048，China
^3.Logistics School，Beijing Wuzi University，Beijing 101149，China

Received:2024-06-25 Revised:2024-09-05 Accepted:2024-09-06 Online:2024-10-08 Published:2025-06-10
Contact: Min ZUO
About author:YAN Wenjing， born in 1985， Ph. D.， lecturer. Her research interests include intelligent processing of biological information， deep learning.
WANG Ruidong， born in 1999， M. S. candidate. His research interests include intelligent processing of biological information， deep learning.
ZUO Min， born in 1973， Ph. D.， professor. His research interests include food big data， deep learning.
ZHANG Qingchuan， born in 1982， Ph. D.， associate professor. His research interests include data mining， natural language processing.
Supported by:
National Key Research and Development Program of China(2022YFF0606803);High-level Research and Innovation Team Program of Beijing Municipal University Teacher Team Construction Support Plan(BPHR20220104);Beijing Scholars Program （099）;State Administration for Market Regulation Project(2023MK169)

基于风味嵌入异构图层次学习的食谱推荐模型

颜文婧¹^,², 王瑞东¹^,², 左敏¹^,³(), 张青川¹^,²

^1.北京工商大学农产品质量安全追溯技术及应用国家工程研究中心，北京 100048
^2.北京工商大学计算机与人工智能学院，北京 100048
^3.北京物资学院物流学院，北京 101149

通讯作者: 左敏
作者简介:颜文婧（1985—），女，安徽淮南人，讲师，博士，主要研究方向：生物信息智能处理、深度学习
王瑞东（1999—），男，内蒙古赤峰人，硕士研究生，主要研究方向：生物信息智能处理、深度学习
左敏（1973—），男，安徽铜陵人，教授，博士，主要研究方向：食品大数据、深度学习 zuomin@btbu.edu.cn
张青川（1982—），男，河北石家庄人，副教授，博士，主要研究方向：数据挖掘、自然语言处理。
基金资助:
国家重点研发计划项目(2022YFF0606803);北京市属高校教师队伍建设支持计划高水平科研创新团队项目(BPHR20220104);北京学者项目(099);国家市场监督管理总局项目(2023MK169)

Abstract

Abstract:

Aiming at the problems of incomplete information dimension， sparse interaction data and redundant interaction information in recipe recommendation tasks， a Recipe recommendation model based on hierarchical learning of Flavor embedding heterogeneous graph （RecipeFlavor） was proposed. Firstly， the flavor molecule dimension was introduced， and a heterogeneous graph was constructed on the basis of users， foods， ingredients and flavor substances of ingredients to represent the connection among four kinds of nodes effectively. Then， a hierarchical learning module based on heterogeneous graph was constructed on the basis of information transmission mechanism， and combined with Squeeze Attention （SA） mechanism， different node relationships were regarded as different information channels， so that key interaction information between nodes was extracted and noise was suppressed. Finally， a Contrastive Learning （CL） module was constructed on the basis of feature-aware noise， and positive and negative sample discrimination tasks were introduced in model learning， thereby enhancing the information associations among users and recipe nodes and improving the model’s learning ability for features. Experimental results show that compared with HGAT （Hierarchical Graph ATtention network for recipe recommendation） model on Recipe 1M+ large dataset， RecipeFlavor has the Area Under the ROC Curve （AUC） increased by 1.44 percentage points， and the model Precision （Pre）， Hit Rate （HR）， Mean Average Precision （MAP）， and Normalized Discounted Cumulative Gain （NDCG） of Top-10 increased by 0.76， 6.11， 2.68， and 3.05 percentage points， respectively. It can be seen that the introduction of flavor molecule information expands the learning dimension of recipe recommendation， and RecipeFlavor can extract key information in heterogeneous graph effectively， and enhance correlation among users and recipes， and thus improving the precision of recipe recommendations.

Key words: Graph Convolution Network (GCN), heterogeneous graph learning, Squeeze Attention (SA) mechanism, recommendation system, Contrastive Learning (CL)

摘要：

针对食谱推荐任务中信息维度不全面、交互数据稀疏和交互信息冗余的问题，提出一种基于风味嵌入异构图层次学习的食谱推荐模型（RecipeFlavor）。首先，引入风味分子维度，并基于用户、食物、食材和食材的风味物质构建异构图，有效表示4种节点之间的联系；其次，基于信息传递机制构建基于异构图的层级学习模块，并结合压缩注意力（SA）机制，将节点的不同关系视为不同的信息通道，提取节点之间的关键交互信息并抑制噪声；最后，基于特征感知噪声构建对比学习（CL）模块，在模型学习中引入正负样本区分任务，增强用户和食谱节点之间的信息关联，提升模型对特征的学习能力。实验结果表明，RecipeFlavor在Recipe 1M+大型数据集上，与HGAT（Hierarchical Graph ATtention network for recipe recommendation）模型相比，曲线下面积（AUC）提升了1.44个百分点，Top-10的模型精确度（Pre）、命中率（HR）、平均精度（MAP）和归一化折损累计增益（NDCG）分别提升了0.76、6.11、2.68和3.05个百分点。可见，风味分子信息的引入拓展了食谱推荐的学习维度，而RecipeFlavor能有效提取异构图中的关键信息，增强用户和食谱之间的关联性，从而提升食谱推荐的精度。

关键词: 图卷积网络, 异构图学习, 压缩注意力机制, 推荐系统, 对比学习

CLC Number:

TP181

Wenjing YAN, Ruidong WANG, Min ZUO, Qingchuan ZHANG. Recipe recommendation model based on hierarchical learning of flavor embedding heterogeneous graph[J]. Journal of Computer Applications, 2025, 45(6): 1869-1878.

颜文婧, 王瑞东, 左敏, 张青川. 基于风味嵌入异构图层次学习的食谱推荐模型[J]. 《计算机应用》唯一官方网站, 2025, 45(6): 1869-1878.

Figures/Tables 15

Fig.1 Framework of RecipeFlavor

Fig.2 Hierarchical graph convolution with SA

Fig.3 CL based on noise augmentation

Tab.1 Information of heterogeneous graph nodes

节点类型	节点数	节点特征维度
用户	7 959	300
食谱	68 794	1 024
食材	8 847	46
风味分子	1 524	256

Tab.2 Information on heterogeneous graph edges

边的类型	边数	边的类型	边数
用户-食谱	135 353	食谱-食谱	647 146
食谱-食材	463 485	食材-食材	146 188
食材-风味分子	588 255	用户-风味分子	1 755 267

Tab.3 Top-K formulation recommendation performance of RecipeFlavor

K	Pre	HR	MAP	NDCG
2	10.94	23.82	20.82	23.82
4	7.44	31.01	23.30	29.06
6	6.04	37.75	24.94	32.78
8	5.27	43.50	26.19	35.85
10	4.88	48.75	27.21	38.48

Tab. 4 Summary of information used in model construction

模型	食谱图像信息	食材信息	营养信息	风味分子信息	关系种类数
MF-BPR	×	×	×	×	1
NGCF	×	×	×	×	1
HAFR	√	√	×	×	1
CMV-SHGNFR	√	√	√	×	2
SCHGN	√	√	√	×	3
HGAT	√	√	√	×	4
RecipeFlavor	√	√	√	√	6

Tab.5 Top-10 experimental results of different models

模型	AUC	HR	NDCG	Pre	MAP
MF-BPR	56.22	7.30	15.68	1.25	4.99
NGCF	58.28	5.20	10.68	3.24	19.48
HAFR	64.35	8.84	18.64
CMV-SHGNFR	68.44	9.55	20.83
SCHGN	72.12	11.05	24.43
HGAT	74.36	32.37	45.70	4.12	24.53
RecipeFlavor	75.80	38.48	48.75	4.88	27.21

Tab.6 Top-10 ablation experimental results

模型	Pre	HR	MAP	NDCG
FRec_base	4.58	45.84	25.47	35.65
FRec_SA	4.65	46.52	26.48	36.49
FRec_NCL	4.68	46.62	25.84	37.47
RecipeFlavor	4.88	48.75	27.21	38.48

Tab.7 Model performance under different loss weight coefficient λ

$λ$	K=1		K=5		K=10
$λ$	HR/%	NDCG/%	HR/%	NDCG/%	HR/%	NDCG/%
0.1	17.90	17.90	31.75	28.55	44.74	35.11
0.2	18.13	18.13	33.52	30.33	46.88	37.05
0.3	18.04	18.04	32.85	29.68	44.92	35.98
0.4	18.10	18.10	33.44	29.85	45.96	36.29
0.5	18.19	18.19	32.99	29.67	46.35	36.23

Tab.7 Model performance under different loss weight coefficient λ

$λ$	K=1		K=5		K=10
$λ$	HR/%	NDCG/%	HR/%	NDCG/%	HR/%	NDCG/%
0.1	17.90	17.90	31.75	28.55	44.74	35.11
0.2	18.13	18.13	33.52	30.33	46.88	37.05
0.3	18.04	18.04	32.85	29.68	44.92	35.98
0.4	18.10	18.10	33.44	29.85	45.96	36.29
0.5	18.19	18.19	32.99	29.67	46.35	36.23

Tab.8 Model performance under different noise constraint υ

$υ$	K=1		K=5		K=10
$υ$	HR/%	NDCG/%	HR/%	NDCG/%	HR/%	NDCG/%
0.1	18.03	18.03	32.37	29.12	45.15	35.54
0.2	18.07	18.07	34.01	30.61	46.57	37.03
0.3	18.00	18.00	33.30	30.04	46.39	36.67
0.4	18.32	18.32	33.43	30.03	46.48	36.54
0.5	17.79	17.79	32.71	29.41	45.08	35.86

Tab.8 Model performance under different noise constraint υ

$υ$	K=1		K=5		K=10
$υ$	HR/%	NDCG/%	HR/%	NDCG/%	HR/%	NDCG/%
0.1	18.03	18.03	32.37	29.12	45.15	35.54
0.2	18.07	18.07	34.01	30.61	46.57	37.03
0.3	18.00	18.00	33.30	30.04	46.39	36.67
0.4	18.32	18.32	33.43	30.03	46.48	36.54
0.5	17.79	17.79	32.71	29.41	45.08	35.86

Tab.9 Model performance under different temperature coefficient τ

$τ$	K=1		K=5		K=10
$τ$	HR/%	NDCG/%	HR/%	NDCG/%	HR/%	NDCG/%
0.05	18.19	18.19	33.52	30.09	47.61	37.53
0.07	18.56	18.56	34.38	30.66	48.61	38.17
0.09	18.38	18.38	34.54	31.07	48.75	38.48
0.11	17.81	17.81	33.46	29.77	48.00	37.25
0.13	18.17	18.17	33.85	30.23	48.22	37.79
0.15	17.66	17.66	34.66	30.83	48.55	38.19

Tab.9 Model performance under different temperature coefficient τ

$τ$	K=1		K=5		K=10
$τ$	HR/%	NDCG/%	HR/%	NDCG/%	HR/%	NDCG/%
0.05	18.19	18.19	33.52	30.09	47.61	37.53
0.07	18.56	18.56	34.38	30.66	48.61	38.17
0.09	18.38	18.38	34.54	31.07	48.75	38.48
0.11	17.81	17.81	33.46	29.77	48.00	37.25
0.13	18.17	18.17	33.85	30.23	48.22	37.79
0.15	17.66	17.66	34.66	30.83	48.55	38.19

Fig.4 Three-dimensional regression visualization of associations among users and recipes

Fig.5 Three-dimensional regression visualization of no association among users and recipes

Fig.6 Dimensionality reduction visualization of relationship features among users and recipes

References 44

1	XIE W， LOU H. Implementation of key technologies for a healthy food culture recommendation system using internet of things［J］. Mobile Information Systems， 2022， 2022： No.9675452.
2	MAJJODI A EL， STARKE A D， TRATTNER C. Nudging towards health？ examining the merits of nutrition labels and personalization in a recipe recommender system ［C］// Proceedings of the 30th ACM Conference on User Modeling， Adaptation and Personalization. New York： ACM， 2022： 48-56.
3	ELSWEILER D， TRATTNER C， HARVEY M. Exploiting food choice biases for healthier recipe recommendation ［C］// Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York： ACM， 2017： 575-584.
4	WU S， SUN F， ZHANG W， et al. Graph neural networks in recommender systems： a survey［J］. ACM Computing Surveys， 2023， 55（5）： No.97.
5	GAO Y， HUANG Z W， HUANG Z Y， et al. Multi-scale broad collaborative filtering for personalized recommendation［J］. Knowledge-Based Systems， 2023， 278： No.110853.
6	ZHANG S， YAO L， SUN A， et al. Deep learning based recommender system： a survey and new perspectives［J］. ACM Computing Surveys， 2020， 52（1）： No.5.
7	MIN W， JIANG S， JAIN R. Food recommendation： framework， existing solutions， and challenges［J］. IEEE Transactions on Multimedia， 2020， 22（10）： 2659-2671.
8	MAHAJAN P， KAUR P D. A systematic literature review of food recommender systems［J］. SN Computer Science， 2024， 5： No.174.
9	RHODES D G， MORTON S， MYROWITZ R， et al. Food and nutrient database for dietary studies 2019-2020： an application database for national dietary surveillance［J］. Journal of Food Composition and Analysis， 2023， 123： No.105547.
10	MARÍN J， BISWAS A， OFLI F， et al. Recipe1M+： a dataset for learning cross-modal embeddings for cooking recipes and food images［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2021， 43（1）： 187-203.
11	GUO Q， ZHUANG F， QIN C， et al. A survey on knowledge graph-based recommender systems ［J］. IEEE Transactions on Knowledge and Data Engineering， 2022， 34（8）： 3549-3568.
12	ZHANG S， LIN X， BAI Z， et al. CGRS： collaborative knowledge propagation graph attention network for recipes recommendation［J］. Connection Science， 2023， 35（1）： No.2212883.
13	GAO X， FENG F， HUANG H， et al. Food recommendation with graph convolutional network［J］. Information Sciences， 2022， 584： 170-183.
14	MORALES-GARZÓN A， GUTIÉRREZ-BATISTA K， MARTIN-BAUTISTA M J. Link prediction in food heterogeneous graphs for personalised recipe recommendation based on user interactions and dietary restrictions ［J］. Computing， 2024， 106（7）： 2133-2155.
15	LI Y， ZHAO F， CHEN Z， et al. Multi-behavior enhanced heterogeneous graph convolutional networks recommendation algorithm based on feature-interaction［J］. Applied Artificial Intelligence， 2023， 37（1）： No.2201144.
16	ZHU S， ZHOU C， PAN S， et al. Relation structure-aware heterogeneous graph neural network ［C］// Proceedings of the 2019 IEEE International Conference on Data Mining. Piscataway： IEEE， 2019： 1534-1539.
17	BING R， YUAN G， ZHU M， et al. Heterogeneous graph neural networks analysis： a survey of techniques， evaluations and applications［J］. Artificial Intelligence Review， 2023， 56（8）： 8003-8042.
18	TIAN Y， ZHANG C， METOYER R， et al. Recipe recommendation with hierarchical graph attention network［J］. Frontiers in Big Data， 2021， 4： No.778417.
19	TIAN Y， ZHANG C， GUO Z， et al. RecipeRec： a heterogeneous graph learning model for recipe recommendation ［C］// Proceedings of the 31st International Joint Conference on Artificial Intelligence. California： ijcai.org， 2022： 3466-3472.
20	SONG Y， YANG X， XU C. Self-supervised calorie-aware heterogeneous graph networks for food recommendation［J］. ACM Transactions on Multimedia Computing Communications and Applications， 2023， 19（1s）： No.27.
21	BONDEVIK J N， BENNIN K E， BABUR Ö， et al. A systematic review on food recommender systems［J］. Expert System with Applications， 2024， 238（Pt E）： No.122166.
22	AHN Y Y， AHNERT S E， BAGROW J P， et al. Flavor network and the principles of food pairing［J］. Scientific Reports， 2011， 1： No.196.
23	左敏，王菲，宋绍义，等. “智慧+食品监管”：发展历程、应用现状与未来方向［J］. 食品科学技术学报， 2024， 42（3）： 1-10.
	ZUO M， WANG F， SONG S Y， et al. “Intelligence+food regulation”： development process， current application status， and future direction［J］. Journal of Food Science and Technology， 2024， 42（3）：1-10.
24	厦门大学. 一种能够提高便利性的食谱推荐系统： 202310497806.5 ［P］. 2023-08-01.
	Xiamen University. A recipe recommendation system that can improve convenience： 202310497806.5 ［P］. 2023-08-01.
25	JIANG X， LU Y， FANG Y， et al. Contrastive pre-training of GNNs on heterogeneous graphs ［C］// Proceedings of the 30th ACM International Conference on Information and Knowledge Management. New York： ACM， 2021： 803-812.
26	LEI Z， UL HAQ A， ZEB A， et al. Is the suggested food your desired？： Multi-modal recipe recommendation with demand-based knowledge graph［J］. Expert Systems with Applications， 2021， 186： No.115708.
27	宋亚光，杨小汕，徐常胜. 跨模态多视角自监督的个性化食谱推荐异构图网络［J］. 计算机辅助设计与图形学学报， 2023， 35（3）： 413-422.
	SONG Y G， YANG X S， XU C S. A cross-modal multi-view self-supervised heterogeneous graph network for personalized food recommendation ［J］. Journal of Computer-Aided Design and Computer Graphics， 2023， 35（3）： 413-422.
28	FOROUZANDEH S， ROSTAMI M， BERAHMAND K， et al. Health-aware food recommendation system with dual attention in heterogeneous graphs［J］. Computers in Biology and Medicine， 2024， 169： No.107882.
29	GAO T， YAO X， CHEN D. SimCSE： simple contrastive learning of sentence embeddings ［C］// Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg： ACL， 2021： 6894-6910.
30	HE K， FAN H， WU Y， et al. Momentum contrast for unsupervised visual representation learning［C］// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2020： 9726-9735.
31	雷景生，剌凯俊，杨胜英，等. 基于上下文语义增强的实体关系联合抽取［J］. 计算机应用， 2023， 43（5）： 1438-1444.
	LEI J S， LA K J， YANG S Y， et al. Joint entity and relation extraction based on contextual semantic enhancement［J］. Journal of Computer Applications， 2023， 43（5）： 1438-1444.
32	YU J L， YIN H Z， GAO M， et al. Socially-aware self-supervised tri-training for recommendation ［C］// Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York： ACM， 2021： 2084-2092.
33	CHEN M， HUANG C， XIA L， et al. Heterogeneous graph contrastive learning for recommendation ［C］// Proceedings of the 16th ACM International Conference on Web Search and Data Mining. New York： ACM， 2023： 544-552.
34	GARG N， SETHUPATHY A， TUWANI R， et al. FlavorDB： a database of flavor molecules ［J］. Nucleic Acids Research， 2018， 46（D1）： D1210-D1216.
35	左敏，胡静珺，颜文婧，等. 基于嗅觉受体激活关系模拟的气味感知预测［J］. 中山大学学报（自然科学版（中英文））， 2024， 63（1）： 86-95.
	ZUO M， HU J J， YAN W J， et al. Prediction of olfactory perception based on simulation of olfactory receptor activation relationships［J］. Acta Scientiarum Naturalium Universitatis Sunyatseni， 2024， 63（1）：86-95.
36	HAUSSMANN S， SENEVIRATNE O， CHEN Y， et al. FoodKG： a semantics-driven knowledge graph for food recommendation ［C］// Proceedings of the 2019 International Semantic Web Conference， LNCS 11779. Cham： Springer， 2019： 146-162.
37	TIAN Y， ZHANG C， METOYER R， et al. Recipe representation learning with networks［C］// Proceedings of the 30th ACM International Conference on Information and Knowledge Management. New York： ACM， 2021： 1824-1833.
38	VAN DEN OORD A， LI Y， VINYALS O. Representation learning with contrastive predictive coding［EB/OL］. ［2024-08-29］..
39	BATRA D， DIWAN N， UPADHYAY U， et al. RecipeDB： a resource for exploring recipes［J］. Database， 2020， 2020： No.baaa077.
40	HE X， LIAO L， ZHANG H， et al. Neural collaborative filtering［C］// Proceedings of the 26th International Conference on World Wide Web. Republic and Canton of Geneva： International World Wide Web Conferences Steering Committee， 2017： 173-182.
41	RENDLE S， FREUDENTHALER C， GANTNER Z， et al. BPR： Bayesian personalized ranking from implicit feedback［C］// Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. Arlington， VA： AUAI Press， 2009： 452-461.
42	WANG X， HE X， WANG M， et al. Neural graph collaborative filtering［C］// Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York： ACM， 2019： 165-174.
43	GAO X， FENG F， HE X， et al. Hierarchical attention network for visually-aware food recommendation［J］. IEEE Transactions on Multimedia， 2020， 22（6）： 1647-1659.
44	ABDI H， WILLIAMS L J. Principal component analysis［J］. WIREs Computational Statistics， 2010， 2（4）： 433-459.

[1]	Zonghang WU, Dong ZHANG, Guanyu LI. Multimodal fusion recommendation algorithm based on joint self-supervised learning [J]. Journal of Computer Applications, 2025, 45(6): 1858-1868.
[2]	Xin CHEN, Zhonghui LIU, Fan MIN. Concept set construction of reduced formal context and its recommendation application [J]. Journal of Computer Applications, 2025, 45(5): 1415-1423.
[3]	Weichao DANG, Xinyu WEN, Gaimei GAO, Chunxia LIU. Multi-view and multi-scale contrastive learning for graph collaborative filtering [J]. Journal of Computer Applications, 2025, 45(4): 1061-1068.
[4]	Renjie TIAN, Mingli JING, Long JIAO, Fei WANG. Recommendation algorithm of graph contrastive learning based on hybrid negative sampling [J]. Journal of Computer Applications, 2025, 45(4): 1053-1060.
[5]	Xiaosheng YU, Zhixin WANG. Sequential recommendation model based on multi-level graph contrastive learning [J]. Journal of Computer Applications, 2025, 45(1): 106-114.
[6]	Tingjie TANG, Jiajin HUANG, Jin QIN, Hui LU. Session-based recommendation based on graph co-occurrence enhanced multi-layer perceptron [J]. Journal of Computer Applications, 2024, 44(8): 2357-2364.
[7]	Jiong WANG, Taotao TANG, Caiyan JIA. PAGCL： positive augmentation graph contrastive learning recommendation method without negative sampling [J]. Journal of Computer Applications, 2024, 44(5): 1485-1492.
[8]	Zhiwen JING, Yujia ZHANG, Boting SUN, Hao GUO. Two-stage recommendation algorithm of Siamese graph convolutional neural network [J]. Journal of Computer Applications, 2024, 44(2): 469-476.
[9]	Beijing ZHOU, Hairong WANG, Yimeng WANG, Lisi ZHANG, He MA. Recommendation method using knowledge graph embedding propagation [J]. Journal of Computer Applications, 2024, 44(10): 3252-3259.
[10]	Li XU, Xiangyuan FU, Haoran LI. Spatial-temporal traffic flow prediction model based on gated convolution [J]. Journal of Computer Applications, 2023, 43(9): 2760-2765.
[11]	Kunpei YE, Xi XIONG, Zhe DING. Recruitment recommendation model based on field fusion and time weight [J]. Journal of Computer Applications, 2023, 43(7): 2133-2139.
[12]	Xuejian ZHAO, Hao LI, Haotian TANG. Recommendation rating prediction algorithm based on user interest concept lattice reduction [J]. Journal of Computer Applications, 2023, 43(11): 3340-3345.
[13]	Chuyuan WEI, Mengke WANG, Chuanhao HU, Guangqi ZHANG. Deep review attention neural network model for enhancing explainability of recommendation system [J]. Journal of Computer Applications, 2023, 43(11): 3443-3448.
[14]	Shuying YANG, Haiming GUO, Xin LI. EEG classification based on channel selection and multi-dimensional feature fusion [J]. Journal of Computer Applications, 2023, 43(11): 3418-3427.
[15]	YAO Huayong, YE Dongyi, CHEN Zhaojiong. Multi-round conversational reinforcement learning recommendation algorithm via multi-granularity feedback [J]. Journal of Computer Applications, 2023, 43(1): 15-21.

Recipe recommendation model based on hierarchical learning of flavor embedding heterogeneous graph

基于风味嵌入异构图层次学习的食谱推荐模型

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 15

References 44

Related Articles 15

Recommended Articles

Metrics