MIGNN: A Multiple Intention-aware Graph Convolutional Neural

Network for POI Recommendation

Jingping Feng

, Mingxin Gan*,

and Yingxue Ma

School of Economics and Management, University of Science and Technology Beijing, Xueyuan Road, Beijing, China

School of Economics and Management, University of Science and Technology Beijing, Beijing, China

Keywords: Recommendation Systems, Deep Learning, Neural Network, Point-Of-Interest Recommendation.

Abstract: In recent years, the rapid development of urbanization and mobile communication motivated the point-of-

interest (POI) recommendation systems. Many models adopt deep learning methods to learn user and POI

embedding, and achieved some improvements. However, existing models seldom pay attention to user check-

in intentions. To address this problem, we propose a novel deep learning model to extract user check-in

intentions using graph network, namely Multiple Intention-aware Graph Convolutional Neural network

(MIGCN). We set four key modules in the proposed model, the embedding module for data preparation, the

intention decomposition module for intention learning, the intention integrating module for intention

embedding generating and the prediction module for future check-in prediction. We carried out a series of

experiments on two real-world datasets. The experimental results verified the superiority of the proposed

model compared with several the state-of-the-art methods.

1 INTRODUCTION

The rapid development of mobile phones and

wearable devices motivated the development of

point-of-interest (POI) recommender systems. In POI

recommendation systems, following the

collaborative filtering (CF) algorithm (Zeng 2021),

the user and the POI are represented by a dense

vector, and the future check-in is predicted by the

matching score of user and POI embeddings.

Recently, researchers attempted to leverage some

context information to improve recommendation

performance, such as geographical location, check-in

time, POI category (Rahmani 2019) and social

network (WANG 2021). Nowadays, many

researchers adopted the graph neural network (GNN)

(Wang 2018) in recommendation systems, to extract

hidden features to generate better user/POI

embedding (Hamilton 2017, Kim 2018). Several

research found that grasping intention features help to

enhance the representations of users (Chang 2020,

Chang 2020). Hence, some researchers proposed

different methods of feature disentangling to discover

user intentions, such as auto-encoders (Tang 2021)

and generative models (Yang 2020), which enhance

the interpretability of user preference (Trieu 2021).

However, the difference between multiple user

intentions were ignored (Chen 2019). Specifically, in

POI recommendation systems, the real-world check-

in is influenced by different intentions. Therefore, it

is necessary to extract different intention features

from user-POI interactions. Similar tasks adopted

heterogeneous graphs to analyse various user

intentions (Guo 2020). For example, the neural graph

collaborative filtering model (NGCF) (Wang 2019),

was proposed in recommendation systems to

decouple user intentions. However, it leaves the work

of distinguish various intentions not well explored,

resulting in the loss of potential available

information. To overcome this problem, we proposed

a multiple intention-aware graph convolutional

neural network (MIGCN) to simultaneously extract

features of various users’ intentions and describe the

weight of different intention embedding.

2 THE FRAMEWORK OF MIGCN

2.1 Overview

As shown in Figure 1, the proposed MIGCN model

contains four modules: embedding module, intention

256

Feng, J., Gan, M. and Ma, Y.

MIGNN: A Multiple Intention-aware Graph Convolutional Neural Network for POI Recommendation.

DOI: 10.5220/0011173400003440

In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 256-261

ISBN: 978-989-758-593-7

decomposition module, intention integration module

and prediction module.

In Embedding Module, we construct

homogeneous graphs for both users and POIs and

generate embedding for users, POIs, and check-ins

via GCN in the embedding module. First, we generate

user embedding and POI embedding in embedding

module for data preparation.

Then, we extract the features of multiple user

intentions by decomposing the user embedding in

user graph constructed by user-POI interactions.

As for the Intention Extraction Module, we

extract the features of user intentions by

disentangling the check-in embedding in the user-

POI graph. By calculating the weight of each

intention, we achieved the fusion of the embedding of

both users and POIs by feeding them into a GCN via

an adjacent matrix. Then, we obtained all the

embedding by disentangling by a multi-head

attention mechanism and input them into the

prediction module. By calculating the weight of each

intention, we fuse the intention features in integration

module.

Finally, we put the intention embedding and POI

embedding into prediction module, to get the

predicted probability of future check-ins. In

Prediction Module, we added an attention mechanism

to form the historical POIs embedding of the target

users, and calculated the weights of historical POIs

for the user. The prediction probability was obtained

by weighting the auxiliary reference value of the

predicted POI (the weight was adjusted as a

hyperparameter). Finally, according to the calculated

prediction probability, we ranked the POIs in

descending order as a recommendation for each user.

2.2 Embedding Module

At the beginning of the model, we use two embedding

layers to conduct the data preparation for intention

feature learning. The user id and POI id are mapped

into two dense vectors by user embedding layer and

POI embedding layer.

*_=

user idu W

(1)

*_=

POI idp W

(2)

Where,

and

represent the weight

matrix in user embedding and POI embedding layers.

Figure 1: Overview of MIGCN model.

2.3 Intention Decomposition Module

As for intention extraction, we first build a intention

decomposition module to extract different weights of

various intentions. Since user intentions existed in

user and POI embedding, we try to explore more

information of the users’ intentions from the

interactions of users and POIs via disentangling the

embedding of the user-POI pair

(,)ui

. Then, we

build two graphs for users and POIs respectively. In

the user-POI graph,

()

←

and

()

←

denote the

message propagation mechanisms for users and POIs

in l-th layer of the GCN.

(1)−l

represents the

presentation of the node and its l-1 order neighbors.

()l

is a parameter matrix, whose parameters are

shared in the same order.

() () ( 1) () ( 1) ( 1)

(*)

−−−

←

llllll

mei eueiWW

(3)

we obtain the updated embedding of users in l-th

layer in the user-POI graph using GCN, as shown in

Equation (4):

() () ()

()

←←

∈



lll

uu u p

eu LeakyRelu m m

(4)

MIGNN: A Multiple Intention-aware Graph Convolutional Neural Network for POI Recommendation

257

The interaction of check-ins is represented by the

Laplace matrix 𝚪

,

, where R is the interaction

matrix between a user and a POI, and D is the degree

matrix. In matrix R, if the user checked-in at the POI,

we define the corresponding value as 1:

[]

ΓD D

(5)

As shown in Equation (6) – (7), after the

propagation in the

l-th layer, we obtain the updated

user embedding

𝑒𝑢

. We also update the POI

embedding, namely

𝑒𝑖

* ()*(1) ()*(1)*(1)

()

−+−

ll ll l

eu LeakyRelu eu eu euΓ+I W ΓW

(6)

* ()*(1) ()*(1)*(1)

()

−+−

ll ll l

ei LeakyRelu ei ei eiΓ+I W ΓW

(7)

After the last layer, and are updated as follows:

=eu eu

(8)

=ei ei

(9)

2.4 Intention Integration

In intention integration module, we use the user-POI

embedding after the message propagation of the

disentangling mechanism to generate the integrated

multiple intention vector

, which was composed of

()l

in l-th layer, as shown in Equation (10). Where

W represents the weight matrix. The weight matrix is

obtained via multi-head attention mechanism, as

shown in Equation (11)-(12).

() (1) (1) (1)

(*)=

heueiW

(10)

()

exp( )

∈



(11)

(1) ()

1..., 1

|| ( )

=−

∈



lkkl

ikK ijj

δα

(12)

Where

()l

represents the coefficient of multi-

head attention.

Figure 2: The explanation of intention decomposition and

integration.

The final intention decoupling is shown in Figure

2. Through decoupling the intention to “accompany

friends”, “need a dress”, “kill time”, and so on of

aspect 1, and “hungry”, “cheap price”, and so on of

aspect 2, we can obtain a more adequate

representation of features.

2.5 Prediction

In order to make the difference between the positive

sample and the negative sample as large as possible,

we use the loss function of BPR. We regard check-in

samples as positive samples

(,)eu ei

, and regarded

no check-in samples as negative samples

(,)

′

eu ei

[50]. To facilitate data processing, we randomly

choose a negative sample to match a positive one. The

formula derivation is shown in Eq. (13).

(,,)

Loss ln ( ) || ||

∈

=−−+



eu ei

eu ei ei O

eu ei

σλθ

(13)

Further, we joined the regularization 𝜆

‖

𝜃

‖



reduce over-fitting. Here, 𝛿 represents the function

of sigmoid. 𝑦

,

indicates that the probability user

will check-in at POI 𝑖 , and 𝑦

,

′

indicates

that the predicted user will not check-in at POI 𝑖

′

3 EXPERIMENTS

3.1 Experiment Settings

Table 1: Experimental datasets.

Datasets Users POIs Check-ins Density

Foursquare 3,705 53,383 836,280 7.2*10

-7

Gowalla 29,858 40,981 1,027,370 6.4*10

-7

To evaluate the performance of MIGCN model, we

choose two real-world check-in datasets, Foursquare

and Gowalla, in our experiments. The datasets cover

user id, POI id and check-in timestamp. The statistic

of the datasets is shown in Table 1. The dataset was

divided into training and test sets in chronological

order, the first 70% check-ins are included in training

set, and the last 30% check-ins are included in test set.

The negative samples are generated by random

sampling.

We adopt two widely used metrics, Recall and

NDCG at top 10 to access the recommendation

performance. We compare the proposed model with

the following baselines: MF (

Lian 2020) is a typical

recommendation method. Both user and POI are

mapped into a hidden space by the matrix

factorization algorithm. NGCF (

Wang 2019) is a

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

258

combination model of graph neural network and

collaborative filtering. The encoding of user-POI

interaction is explicitly embedded in the embedding

representation. DGCF (

Wang 2020) is a model based

on graph neural network for feature disentangling,

which combines a new neighbor routing mechanism

into a message propagation mechanism.

3.2 Performance Comparison

We compared the proposed MIGCN model with the

baselines on two datasets in terms of Recall and

NDCG.

The experimental results are shown in Table 2:

The proposed model outperforms the traditional

MF model. For example, in terms of Recall, MIGCN

achieves ~45.11% improvement compared with MF.

In terms of NDCG, MIGCN achieves ~43.82%

improvement compared with MF.

As for the advance graph neural network model,

such as NGCF and DGCF, MIGCN also achieves

significant improvement. For example, MIGCN

achieves ~19.79% and ~14.29% improvements than

NGCF and DGCF models in terms of Recall, and

achieves ~25.38% and ~19.27% improvements than

NGCF and DGCF models in terms of NDCG.

These results illustrate the MIGCN model was

better than MF, NGCF, DGCF, and verify the

superiority of the weighted intention decoupling and

intention combination in the proposed model.

Table 2: Performance of models.

Dataset Metric MF NGCF

Gowalla

Recall@10 0.3137 0.3800

Recall@20 0.4632 0.5200

NDCG@10 0.3400 0.3900

NDCG@20 0.4900 0.5250

Foursquare

Recall@10 0.3240 0.3890

Recall @20 0.4770 0.5380

NDCG@10 0.3500 0.3920

NDCG@20 0.5200 0.5960

Dataset Metric DGCF MIGCN

Gowalla

Recall@10 0.3983 0.4552

Recall@20 0.5410 0.6152

NDCG@10 0.4100 0.4890

NDCG@20 0.5310 0.5860

Foursquare

Recall@10

0.4150 0.4452

Recall @20 0.5760 0.6350

NDCG@10 0.4130 0.4991

NDCG@20 0.6030 0.6130

3.3 Visualization of Check-in based on

MIGCN

Figure 3: Visualization of Check-in based on MIGCN.

In order to explore the ability of the proposed

model in depicting different user intentions, we

visualize the intention vectors of several selected

users. as shown in Figure 3, we find that user

intentions in different aspects have different weights,

which determines user's check-in decision in real-

world. The differences in the colour of the four

intention aspects show that the proposed model

indeed distinguish different user intentions.

Figure 4: Visualization of weights in multi-head attention.

Further, we visualize the weights in multi-head

attention module to verify the positive effect of

attention mechanism on intention feature learning. As

shown in Figure 4. The dimensions of x axis and y

axis represented the six dimensions of user intention

feature, and different shades of colour represent

different weights of attention. From Figure 3 (a), we

see the red area on the (5, 5) head, which illustrated

that the intention represented by the 5-th head is the

most important one for predicting user check-ins.

However, the 0-th head has tiny contribution on

future check-in prediction, as the area around the 0-th

head is filled with the lightest colour. As shown in

Figure 3 (b), the (3, 5) head is coloured in dark blue,

it illustrated that the 3-th head had the great

importance on MIGCN.

MIGNN: A Multiple Intention-aware Graph Convolutional Neural Network for POI Recommendation

259

4 CONCLUSION AND FUTURE

WORK

There may be multiple intentions that motivate users

to check-in in real life. We think different intentions

have different influence on user’s check-in decision.

Only a few existing studies address the learning of

multiple intentions. However, using the features of

multiple intention in recommendation algorithm is

conducive to the understanding of user preference,

and then to improve the recommendation

performance. In this paper, we aim to design an

intention representation model to enrich the

characterization of users and POIs for

recommendation. To make up for the deficiency of

GNN, we used the multi-head attention mechanism

and self-attention mechanism to focus on more

important POIs. The designs of the intention

extraction module and prediction module can capture

complex relationships between users and POIs,

hence, we can learn features and obtain more accurate

recommendations. Furthermore, the intention that is

extracted from our model has the ability to explain

the user’s check-in. This helps alleviate the problem

of insufficient features. We conducted a series of

experiments on two datasets to verify the

effectiveness of the proposed model. The comparison

results show that the proposed model outperforms the

state-of-the-art recommendation models.

For discussion, we attribute the effective

performance of the proposed model into the

following aspects:

(1) The proposed multiple intention graph neural

network model not only effectively describes user’s

multiple intention, but also calculates the weights of

different intentions from different views when

integrating it.

(2) The proposed method conducts an exhaustive

mining from user-POI interactions, it aggregated and

updated the embedding vectors of users and POIs.

Through the analysis of the datasets, we found that

users generally have different check-ins under

different intentions.

(3) The proposed model adopted an attention

mechanism to capture user intention in different

layers. We added multi-head attention mechanism in

the proposed model to integrate the multiple intention

features, serving to future prediction.

(4) The proposed model combines different

intention features with the model of historical check-

in interactions. The greater the weight, the more

important the corresponding intention feature plays in

future check-in prediction.

However, we do not consider other information,

such as comments, time, etc., which results in single-

dimensional information and thus cannot

dynamically capture dynamic user preference. In

future work, we will introduce additional auxiliary

information to capture dynamic changes for user

intention and further interpret dynamic intention

representations, such as in conjunction with the

temporal information of check-in data, which will

make feature representation more complete.

ACKNOWLEDGEMENTS

This research was supported by the National Natural

Science Foundation of China (Nos. 71871019,

71471016, 71729001).

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