Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (2): 66-75.doi: 10.6040/j.issn.1672-3961.0.2019.304

• Machine Learning & Data Mining • Previous Articles     Next Articles

Entity recommendation based on normalized similarity measure of meta graph in heterogeneous information network

Wenkai ZHANG(),Ke YU,Xiaofei WU   

  1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2019-06-13 Online:2020-04-20 Published:2020-04-16
  • Supported by:
    国家自然科学基金资助项目(61601046);国家自然科学基金资助项目(61171098);中国111基地资助项目(B08004);欧盟FP7 IRSES资助项目(612212)

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Abstract:

Based on the promising result of meta graph in heterogeneous information networks (HIN), normalized similarity measure of meta graph (NSMG) was proposed which combined implicit feedback matrix and PathSim(meta path-based similarity) to solve the problem of preference for large degree entities. Yelp-HIN(heterogeneous information networks in Yelp) and Amazon-HIN(heterogeneous information networks in Amazon) were constructed based on Yelp and Amazon datasets. Different types of meta graphs and normalized similarity measures were defined. Matrix decomposition and factorization machine were used to combine the similarities on different meta graphs. The experimental results showed that the proposed method based on normalization similarity measure of meta graphs performed better than the commonly used entity recommendation method in HIN on very sparse data sets.

Key words: heterogeneous information networks, meta graph, normalized similarity measure, entity recommendation, matrix decomposition, factorization machine

CLC Number: 

  • TP311

Fig.1

Schema of Yelp-HIN network"

Fig.2

Schema of Amazon-HIN network"

Fig.3

Meta graphs in Yelp-HIN/Amazon-HIN"

Table 1

Meta paths in Yelp-HIN and Amazon-HIN"

HIN 元路径
Yelp-HIN M3:用户-商家-分类-商家
M4:用户-商家-州-商家
M5:用户-商家-城市-商家
M6:用户-商家-星级-商家
M7:用户-商家-分类-商家-分类-商家
M8:用户-商家-州-商家-州-商家
M9:用户-评论-主题-评论-用户-商家
M10:用户-评论-商家-评论-用户-商家
Amazon-HIN M3:用户-产品-分类-产品
M4:用户-产品-品牌-产品
M5:用户-产品-分类-产品-分类-产品
M6:用户-产品-品牌-产品-品牌-产品
M7:用户-评论-主题-评论-用户-产品
M8:用户-评论-产品-评论-用户-产品

Fig.4

Symmetrical part and user implicit feedback part of the meta graph"

Fig.5

Unified representation of the item-symmetric meta graph M1"

Fig.6

Unified representation of the user-symmetric meta graph M2"

Table 2

Statistics of Yelp-50k and Amazon-50k"

统计信息 用户数/个 商家/个 类别数/个 星级数/个 城市数/个 州数/个 品牌数/个 签到数/个 签到数:用户数 签到数:商家数
Yelp-50k 13 663 8 164 716 9 154 13 37 659 2.76 4.61
Amazon-50k 20 345 8 146 488 640 29 350 1.44 3.60

Table 3

Sparse comparison of datasets"

数据集 稠密度/%
Yelp-50k 0.037
Amazon-50k 0.017
IMDb-MovieLens-100k 6.988
Yelp[19] 0.086
Douban 0.630

Table 4

Performance comparison"

方法 Yelp-50k Amazon-50k
RMSE NSMG提升百分比/% RMSE NSMG提升百分比/%
RSVD 2.599 6 52.6 2.639 5 55.5
HeteRec 1.732 0 28.9 1.936 0 39.3
FMG 1.247 3 1.2 1.185 0 1.3
NSMG 1.231 8 1.174 1

Fig.7

Performance comparison on each type ofmeta graph"

Fig.8

Performance with single meta graph on Yelp-HIN and Amazon-HIN"

Fig.9

Parameter tuning on Yelp-50k and Amazon-50k"

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