融合社交地理信息加权矩阵分解的兴趣点推荐算法

doi:10.13229/j.cnki.jdxbgxb.20211201

Abstract

Abstract:

The point-of-interest （POI） recommendation services provided by the location-based social network （LBSN） have become an important means of mining users' preference for POIs. The sparsity of user-POI matrix is the primary problem to be solved， and a large number of unknown values in implicit feedback cannot reflect user preferences. To improve recommendation precision， this paper proposes a point of interest recommendation algorithm integrating social geographical information based on weighted matrix factorization （SGWMF）. The social information is modeled through the power-law distribution. The check-in information of the user's friends is converted into the user's visit location preference. Secondly， the power-law distribution of geographical information is used to construct the user's visit location preference matrix to alleviate the data sparsity problem. Thirdly， in order to extend the effectiveness of the model， we improve the objective function by adding implicit feedback term. Finally， the experimental results on two datasets show that it has better performance than other POI recommendation algorithms and can improve the accuracy of recommendation results.

Key words: computer application, social geographical information, weighted matrix factorization, point-of-interest （POI） recommendation

CLC Number:

TP391

Ying HE,Zhuo-ran WANG,Xu ZHOU,Yan-heng LIU. Point of interest recommendation algorithm integrating social geographical information based on weighted matrix factorization[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(9): 2632-2639.

Figures/Tables 8

Table 1

Parameters table"

参数	定义	参数	定义
$U$	数据集中所有用户的集合	$R$	用户-地点访问频率矩阵
$u$	某个用户 $u ∈$ $U$	$r u, l$	用户 $u$ 在地点 $l$ 的访问频率
$L$	数据集中所有地点的集合	$O u$	用户 $u$ 的地点簇类
$l$	某个地点 $l ∈$ $L$	$B u$	用户 $u$ 的中心位置集
$C$	用户签到0/1矩阵	$b n$	用户 $u$ 在簇 $n$ 下的中心位置
$S$	用户社交关系矩阵	$z$	用户好友的签到频率
$s u, u'$	用户 $u$ 与用户 $u'$ 有好友关系

Table 1

Table 2

Fig.1

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参数	数据集
参数	Gowalla	Brightkite
签到数量	1 278 274	4 747 281
用户数量	18 737	51 406
兴趣点数量	32 510	772 966
好友关系数量	86 985	428 156
用户-兴趣点的矩阵密度/10^-5	130	2.7084

Point of interest recommendation algorithm integrating social geographical information based on weighted matrix factorization

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