Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (4): 1122-1128.DOI: 10.11772/j.issn.1001-9081.2022030455
Special Issue: 数据科学与技术
• Data science and technology • Previous Articles Next Articles
Canghong JIN1,2(), Yuhua SHAO2, Qinfang HE2
Received:
2022-04-11
Revised:
2022-08-08
Accepted:
2022-08-15
Online:
2023-01-11
Published:
2023-04-10
Contact:
Canghong JIN
About author:
SHAO Yuhua, born in 2001. His research interests include recommender system.Supported by:
通讯作者:
金苍宏
作者简介:
邵育华(2001—),男,浙江杭州人,主要研究方向:推荐系统;基金资助:
CLC Number:
Canghong JIN, Yuhua SHAO, Qinfang HE. Long-tail recommendation model based on adaptive group reranking[J]. Journal of Computer Applications, 2023, 43(4): 1122-1128.
金苍宏, 邵育华, 何琴芳. 基于自适应群组重排的长尾推荐模型[J]. 《计算机应用》唯一官方网站, 2023, 43(4): 1122-1128.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022030455
模型 | N | MovieLens 1M | Yahoo | 模型 | N | MovieLens 1M | Yahoo | ||||
---|---|---|---|---|---|---|---|---|---|---|---|
精度/% | 覆盖率/% | 精度/% | 覆盖率/% | 精度/% | 覆盖率/% | 精度/% | 覆盖率/% | ||||
ItemCF[ | 5 | 43.20 | 2.96 | 25.20 | 3.94 | CDAE[ | 5 | 39.07 | 14.60 | 40.01 | 4.41 |
10 | 39.20 | 6.43 | 20.50 | 6.84 | 10 | 34.34 | 18.97 | 36.56 | 7.07 | ||
20 | 31.50 | 12.45 | 16.35 | 17.63 | 20 | 29.96 | 23.73 | 31.78 | 11.47 | ||
40 | 25.60 | 25.51 | 12.07 | 46.64 | 40 | 25.05 | 32.76 | 26.97 | 18.19 | ||
80 | 19.14 | 49.90 | 7.85 | 82.02 | 80 | 19.58 | 43.06 | 21.18 | 29.01 | ||
LFM[ | 5 | 22.00 | 0.66 | 22.08 | 1.51 | VASP[ | 5 | 33.23 | 6.23 | 30.13 | 5.10 |
10 | 19.90 | 1.17 | 18.55 | 2.44 | 10 | 29.65 | 9.76 | 27.01 | 8.90 | ||
20 | 17.05 | 1.94 | 13.75 | 3.94 | 20 | 23.57 | 13.63 | 22.67 | 13.12 | ||
40 | 13.75 | 3.37 | 10.22 | 6.84 | 40 | 19.60 | 20.79 | 18.98 | 16.67 | ||
80 | 10.25 | 5.87 | 6.93 | 12.88 | 80 | 15.66 | 25.49 | 14.80 | 22.28 | ||
UserCF[ | 5 | 43.80 | 8.69 | 23.80 | 8.15 | MDOM[ | 5 | 23.29 | 4.86 | 15.60 | 15.66 |
10 | 39.00 | 13.56 | 19.00 | 16.20 | 10 | 18.52 | 8.95 | 12.06 | 28.19 | ||
20 | 33.60 | 22.57 | 15.25 | 26.17 | 20 | 15.34 | 16.51 | 10.08 | 50.35 | ||
40 | 25.77 | 33.93 | 10.78 | 54.06 | 40 | 12.20 | 28.38 | 7.35 | 85.03 | ||
80 | 19.29 | 52.09 | 7.10 | 87.57 | 80 | 8.71 | 57.99 | 5.45 | ― | ||
NSGA-Ⅱ-CF-RS[ | 5 | 17.21 | 4.13 | 10.84 | 15.18 | AGRM | 5 | 21.80 | 6.56 | 15.80 | 16.19 |
10 | 15.25 | 6.67 | 7.14 | 25.83 | 10 | 18.50 | 9.36 | 13.40 | 27.84 | ||
20 | 15.61 | 13.78 | 6.39 | 37.24 | 20 | 16.60 | 15.47 | 10.75 | 49.37 | ||
40 | 12.17 | 24.09 | 5.36 | 66.82 | 40 | 12.17 | 28.17 | 7.95 | 83.16 | ||
80 | 8.58 | 41.91 | 3.44 | ― | 80 | 8.56 | 58.24 | 5.50 | ― |
Tab. 1 Precisions and coverages of different models on MovieLens 1M and Yahoo datasets
模型 | N | MovieLens 1M | Yahoo | 模型 | N | MovieLens 1M | Yahoo | ||||
---|---|---|---|---|---|---|---|---|---|---|---|
精度/% | 覆盖率/% | 精度/% | 覆盖率/% | 精度/% | 覆盖率/% | 精度/% | 覆盖率/% | ||||
ItemCF[ | 5 | 43.20 | 2.96 | 25.20 | 3.94 | CDAE[ | 5 | 39.07 | 14.60 | 40.01 | 4.41 |
10 | 39.20 | 6.43 | 20.50 | 6.84 | 10 | 34.34 | 18.97 | 36.56 | 7.07 | ||
20 | 31.50 | 12.45 | 16.35 | 17.63 | 20 | 29.96 | 23.73 | 31.78 | 11.47 | ||
40 | 25.60 | 25.51 | 12.07 | 46.64 | 40 | 25.05 | 32.76 | 26.97 | 18.19 | ||
80 | 19.14 | 49.90 | 7.85 | 82.02 | 80 | 19.58 | 43.06 | 21.18 | 29.01 | ||
LFM[ | 5 | 22.00 | 0.66 | 22.08 | 1.51 | VASP[ | 5 | 33.23 | 6.23 | 30.13 | 5.10 |
10 | 19.90 | 1.17 | 18.55 | 2.44 | 10 | 29.65 | 9.76 | 27.01 | 8.90 | ||
20 | 17.05 | 1.94 | 13.75 | 3.94 | 20 | 23.57 | 13.63 | 22.67 | 13.12 | ||
40 | 13.75 | 3.37 | 10.22 | 6.84 | 40 | 19.60 | 20.79 | 18.98 | 16.67 | ||
80 | 10.25 | 5.87 | 6.93 | 12.88 | 80 | 15.66 | 25.49 | 14.80 | 22.28 | ||
UserCF[ | 5 | 43.80 | 8.69 | 23.80 | 8.15 | MDOM[ | 5 | 23.29 | 4.86 | 15.60 | 15.66 |
10 | 39.00 | 13.56 | 19.00 | 16.20 | 10 | 18.52 | 8.95 | 12.06 | 28.19 | ||
20 | 33.60 | 22.57 | 15.25 | 26.17 | 20 | 15.34 | 16.51 | 10.08 | 50.35 | ||
40 | 25.77 | 33.93 | 10.78 | 54.06 | 40 | 12.20 | 28.38 | 7.35 | 85.03 | ||
80 | 19.29 | 52.09 | 7.10 | 87.57 | 80 | 8.71 | 57.99 | 5.45 | ― | ||
NSGA-Ⅱ-CF-RS[ | 5 | 17.21 | 4.13 | 10.84 | 15.18 | AGRM | 5 | 21.80 | 6.56 | 15.80 | 16.19 |
10 | 15.25 | 6.67 | 7.14 | 25.83 | 10 | 18.50 | 9.36 | 13.40 | 27.84 | ||
20 | 15.61 | 13.78 | 6.39 | 37.24 | 20 | 16.60 | 15.47 | 10.75 | 49.37 | ||
40 | 12.17 | 24.09 | 5.36 | 66.82 | 40 | 12.17 | 28.17 | 7.95 | 83.16 | ||
80 | 8.58 | 41.91 | 3.44 | ― | 80 | 8.56 | 58.24 | 5.50 | ― |
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