Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (3): 920-933.doi: 10.13229/j.cnki.jdxbgxb20180193

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TPR⁃TF： time⁃aware point of interest recommendation model based on tensor factorization

Nan WANG1,2(),Jin⁃bao LI1,2(),Yong LIU1,2,Yu⁃jie ZHANG1,2,Ying⁃li ZHONG1,2   

  1. 1. School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
    2. Key Laboratory of Database and Parallel Computing, Heilongjiang University, Harbin 150080, China
  • Received:2018-03-05 Online:2019-05-01 Published:2019-07-12
  • Contact: Jin?bao LI E-mail:wangnn125@126.com;jbli@hlju.edu.cn

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Abstract:

With the rapid growth of the location?based social networks, Point of Interest (POI) recommen?dation has become an important research topic in the field of data mining. Existing approaches for POI recommendation task do not reasonably utilize the time sensitivity of POI recommendations and have not taken full account of the user's behavior preferences at different time periods, causing the POI recommendation performance is poor. Firstly, this paper studies the POI recommendation problem of time sensitivity and proposes a time dynamic partition algorithm based on hierarchical clustering. Through partition the fine grain of time, the result of the experiment is more reasonable and effective than the previous experiments which partition time is evenly given by experience. Secondly, by combining the time?aware recommendation with the influence of the user's direct friendship and potential friendship, the paper expands the scope of user's social influence, and then further improves the POI recommendation performance. Lastly, using the method of randomly selecting POIs by the frequency distribution of check?ins, it improves the classic BPR method. Experimental results on the two datasets indicate that the TPR?TF model is superior to the current mainstream POI recommendation models, in terms of precision and recall.

Key words: computer application, point of interest(POI) recommendation, tensor factorization, time?aware, social relationship

CLC Number: 

  • TP391

Table1

Notations"

符号 描 述
U、T、L 分别表示用户集合、时间戳集合、POI位置集合。
D D = { d i = ( u , t , l ) ( u , t , l ) ∈ U × T × L } ， D 为全体签到记录集合，其中 d i 为用户?时间?位置三维张量上的第 i 条签到记录， ( u , t , l ) 为用户 u 在时间 t 在位置 l 的一条签到记录。
U u、 L l、 T t U u、 L l、 T t ∈ R k 分别为用户 u 的特征向量、POI位置 l 的特征向量、时间戳 t 所在时间分段的特征向量， k 为特征向量的维度。
f ( u , t , l ) 用户 u 在时间 t 访问位置 l 的可能的打分函数。
w i , j s , w i , j c , w i , j w i , j s 为用户 i 和其朋友基于共同朋友的相似性； w i , j c 为用户 i 和其朋友 j 基于共同签到的相似性； w i , j 为用户 i 和其朋友 j 基于共同朋友和共同签到的相似性。
A ( i ) 表示用户 i 访问过的所有POI位置的集合。
l i > u , t l j 对于 ? l i , l j ∈ L ,用户 u 在时间t优先选择 l i 而非 l j 的一种顺序关系。
L soc?fri、L soc?undirect?fri 分别为直接朋友关系的社交影响正则化项、潜在朋友关系的社交影响正则化项。
θ、γ、μ、λ 分别为参数集合、步长、朋友正则化项系数以及与 U u 、 L l 、 T t 有关的正则化项系数。

Table2

Statistics of Gowalla and Brightkite"

类 别 Gowalla Brightkite
用户数 26 287 24 227
POI数 11 640 43 592
签到记录数 1 104 464 3 260 809

Fig.1

Effect of different parameter settings inTPR?TF model"

Table 3

Statistical results of different time partition methods in Gowalla(ns=4)"

序号 常规时间分段 时间动态分段
T1 00:00~06:00 00:08:55~06:57:60
T2 06:00~12:00 06:57:60~13:24:03
T3 12:00~18:00 13:24:03~19:00:01
T4 18:00~00:00 19:00:01~00:08:55

Table 4

Statistical results of different time partition"

序号 常规时间分段 时间动态分段
T1?工作日 00:00~06:00 00:04:40~05:36:15
T2?工作日 06:00~12:00 05:36:15~10:41:59
T3?工作日 12:00~18:00 10:41:59~16:16:48
T4?工作日 18:00~00:00 16:16:48~00:04:40
T5?非工作日 00:00~06:00 04:02:08~07:48:44
T6?非工作日 06:00~12:00 07:48:44~12:43:10
T7?非工作日 12:00~18:00 12:43:10~16:13:57
T8?非工作日 18:00~00:00 16:13:57~04:02:08

Fig.2

Performance comparison on different time partition (ns=8)"

Fig.3

Performance comparison of different social relationships"

Fig.4

Performance comparison of TPR?TF andTPR?TF_BPR"

Fig.5

Performance comparison of TPR?TF andother models"

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