Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (6): 1683-1688.DOI: 10.11772/j.issn.1001-9081.2021081417
• National Open Distributed and Parallel Computing Conference 2021 (DPCS 2021) • Previous Articles
Tengyue HAN1, Shaozhang NIU1(), Wen ZHANG2
Received:
2021-08-06
Revised:
2021-10-15
Accepted:
2021-10-29
Online:
2022-06-22
Published:
2022-06-10
Contact:
Shaozhang NIU
About author:
HAN Tengyue, born in 1990, Ph. D. candidate. Her research interests include recommendation algorithm, data mining.Supported by:
通讯作者:
牛少彰
作者简介:
韩滕跃(1990—),女,河北衡水人,博士研究生,主要研究方向:推荐算法、数据挖掘基金资助:
CLC Number:
Tengyue HAN, Shaozhang NIU, Wen ZHANG. Multimodal sequential recommendation algorithm based on contrastive learning[J]. Journal of Computer Applications, 2022, 42(6): 1683-1688.
韩滕跃, 牛少彰, 张文. 基于对比学习的多模态序列推荐算法[J]. 《计算机应用》唯一官方网站, 2022, 42(6): 1683-1688.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021081417
数据集 | 用户 数目 | 商品 数目 | 交互 数目 | 类别 数目 | 品牌 数目 |
---|---|---|---|---|---|
衣物 | 39 386 | 23 010 | 278 406 | 715 | 3 964 |
手机 | 27 804 | 10 192 | 191 396 | 49 | 5 418 |
Tab. 1 Statistics of datasets for experiments
数据集 | 用户 数目 | 商品 数目 | 交互 数目 | 类别 数目 | 品牌 数目 |
---|---|---|---|---|---|
衣物 | 39 386 | 23 010 | 278 406 | 715 | 3 964 |
手机 | 27 804 | 10 192 | 191 396 | 49 | 5 418 |
参数名称 | 参数值 |
---|---|
Batch_size(visual) | 100 |
Learning_rate(visual) | 0.000 1 |
0.5 | |
Image_size | (96,96,3) |
Feature_dimension | 50 |
Layers_of_RNN | 1 |
Batch_size | 200 |
Initial_learning_rate | 0.01 |
Step_size | 5 |
Tab. 2 Experimental parameter setting
参数名称 | 参数值 |
---|---|
Batch_size(visual) | 100 |
Learning_rate(visual) | 0.000 1 |
0.5 | |
Image_size | (96,96,3) |
Feature_dimension | 50 |
Layers_of_RNN | 1 |
Batch_size | 200 |
Initial_learning_rate | 0.01 |
Step_size | 5 |
数据集 | 算法 | Hit-Ratio@10 | NDCG@10 |
---|---|---|---|
衣物 | VBPR | 0.175 | 0.087 |
MV-RNN | 0.342 | 0.199 | |
GRU4Rec | 0.276 | 0.156 | |
Caser | 0.283 | 0.162 | |
SASRec | 0.382 | 0.227 | |
BERT4Rec | 0.387 | 0.233 | |
LESSR | 0.396 | 0.244 | |
本文算法 | 0.423 | 0.261 | |
手机 | VBPR | 0.278 | 0.156 |
MV-RNN | 0.535 | 0.332 | |
GRU4Rec | 0.441 | 0.270 | |
Caser | 0.497 | 0.317 | |
SASRec | 0.565 | 0.361 | |
BERT4Rec | 0.578 | 0.367 | |
LESSR | 0.583 | 0.374 | |
本文算法 | 0.599 | 0.383 |
Tab. 3 Comparison with classical sequential recommendation algorithms
数据集 | 算法 | Hit-Ratio@10 | NDCG@10 |
---|---|---|---|
衣物 | VBPR | 0.175 | 0.087 |
MV-RNN | 0.342 | 0.199 | |
GRU4Rec | 0.276 | 0.156 | |
Caser | 0.283 | 0.162 | |
SASRec | 0.382 | 0.227 | |
BERT4Rec | 0.387 | 0.233 | |
LESSR | 0.396 | 0.244 | |
本文算法 | 0.423 | 0.261 | |
手机 | VBPR | 0.278 | 0.156 |
MV-RNN | 0.535 | 0.332 | |
GRU4Rec | 0.441 | 0.270 | |
Caser | 0.497 | 0.317 | |
SASRec | 0.565 | 0.361 | |
BERT4Rec | 0.578 | 0.367 | |
LESSR | 0.583 | 0.374 | |
本文算法 | 0.599 | 0.383 |
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