Embedding-Enhanced Similarity Metrics for Next POI Recommendation

Sara Jarrad

1

, Hubert Naacke

1

, Stephane Gancarski

1

and Modou Gueye

2

1

LIP6, Sorbonne University, Paris, France

2

Department of Mathematics and Computer Science, Cheikh Anta Diop University, Dakar, Senegal

Keywords:

Point of Interest (POI), Next POI Recommendation, Word Embedding, Word2Vec, Similarity Metrics.

Abstract:

Social media platforms allow users to share information, including photos and tags, and connect with their

peers. This data can be used for innovative research, such as proposing personalized travel destination recom-

mendations based on user-generated traces. This study aims to demonstrate the value of using embeddings,

which are dense real-valued vectors representing each visited location, in generating recommendations for the

next Point of Interest (POI) to visit based on the last POI visited. The Word2Vec language model is used to

generate these embeddings by considering POIs as words and sequences of POIs as sentences. This model

captures contextual information and identiﬁes similar contexts based on the proximity of numerical vectors.

Empirical experiments conducted on a real dataset show that embedding-based methods outperform conven-

tional methods in predicting the next POI to visit.

1 INTRODUCTION

Social networks provide valuable information on user

mobility and behavior, with geolocated data allowing

the identiﬁcation of users’ itineraries and POIs vis-

ited. We are interested in the task of predicting the

next POI, which is of primary importance not only

for tourism but also for discovering new areas.

The challenges we face include data densiﬁcation,

and using embeddings for next POI recommendation.

An active line of research is the use of language

models for recommendation tasks. However, most of

existing solutions focus on recommending items (e.g.,

products, movies) which is based on personal prefer-

ences and behavioral data, does not consider the se-

quential context and characteristics of mobility trajec-

tories. Therefore they are not applicable to the next

POI recommendation problem.

In this study, since user check-ins (photos labeled

with place and time) are sequential data, we target

the Word2Vec model for its efﬁciency in handling

sequential data, and investigate whether embeddings

provide beneﬁts over classical recommendation meth-

ods. We have the following contributions:

• POI extraction and dataset construction.

• Embedding-enhanced similarity functions for tra-

jectory comparison.

• Solution’s implementation on the Spark parallel

computing engine, chosen for its speed and ability

to perform distributed processing, and computa-

tions on large-scale datasets for complex analysis.

• Extensive experiments using a large-scale

dataset of geolocated photos collected from

YFCC100M (Thomee et al., 2016), along with an

in-depth analysis of different parameters’ impact

on the prediction’s quality.

2 PROBLEM DEFINITION

Neural networks, particularly the Word2Vec

model (Mikolov et al., 2013), have gained at-

tention for their success in various NLP tasks. A line

of work investigates the potential of embeddings for

recommendation tasks (Grbovic et al., 2015).

This study aims to compare the effectiveness of

embeddings with classical methods that don’t use

them for next POI recommendation. However, this

raises two problems. Firstly, to learn Word2Vec em-

beddings, a dataset of sentences with a common vo-

cabulary is required, which creates a density prob-

lem when applied to check-ins data. Secondly, since

next POI recommendation is a collaborative ﬁltering

method, ﬁnding similar trajectories to a given one is

essential, posing a trajectory similarity problem.

Jarrad, S., Naacke, H., Gancarski, S. and Gueye, M.

Embedding-Enhanced Similarity Metrics for Next POI Recommendation.

DOI: 10.5220/0012060300003541

In Proceedings of the 12th International Conference on Data Science, Technology and Applications (DATA 2023), pages 247-254

ISBN: 978-989-758-664-4; ISSN: 2184-285X

Copyright

c

2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

247

2.1 Dataset Density Problem

We consider a dataset that only includes check-ins

made by users and their geo positions (latitude and

longitude). Our study is based on the YFCC dataset,

which has a low number of check-ins per geo posi-

tion, with an average of only 4 check-ins.

However, to recommend POIs based on user

check-ins, we need locations that have been checked

in by many users, which is not the case for our dataset.

Therefore, we aim to increase the dataset’s density.

Let D be a dataset, its density, expressed as the

average number of check-ins per position, is denoted

d(D). The dataset density problem can be stated as

follows: given a dataset D such that d(D) = d

0

, and

a higher density value d

1

to reach (d

1

> d

0

), ﬁnd sets

of close POIs to merge, which results in a dataset D

′

such that d(D

′

) = d

1

.

Figure 1: POIs identiﬁcation from users check-ins.

In Figure 1, we illustrate that problem where three

users share no position (upper subﬁgure). Merging

three close positions into a single POI allows to con-

struct trajectories that share one POI (lower subﬁg-

ure).

2.2 Trajectory Similarity and POI

Ranking Problems

The goal of POI recommendation is to suggest POIs

to visit based on social media users’ records and

traces. A trajectory t in the Train Set represents a

sequence of visited POIs (p

1

,·· · , p

n

). Our task is to

recommend the next POI to visit after the last POI p

n

in a given trajectory t. This involves ﬁnding a set of

candidate trajectories in Train Set that contain a path

(p

n

, p

r

), and selecting the best POI from among the

possible p

r

points. Our focus is on the ﬁrst step of

this process, which involves ﬁnding similar trajecto-

ries that contain p

n

using a similarity function sim.

We also use a value k for the top k most similar tra-

jectories and a POI ranking function rank to select the

highest rank POI in step 2.

3 RELATED WORKS

Location prediction is a challenge in human mobil-

ity modeling that predicts a user’s location using their

historical traces.

Recently, neural networks have gained attention

due to the success of word embeddings in various

NLP tasks, among them the W2V model described

in (Mikolov et al., 2013).

(Grbovic et al., 2015) proposes an algorithm

called Prod2vec. It generates product embeddings

from purchase sequences and performs recommenda-

tions based on the most similar products in the vector

space obtained using W2V on product sequences.

However, this work targets products recommen-

dation, which is often based on personal preferences

and behavioral data, while POIs recommendation of-

ten involve a deeper understanding of the proposed

trajectories. Moreover, (Grbovic et al., 2015) assumes

that the products are already predeﬁned in the dataset,

so it does not solve our problem.

Among the works based on embeddings extracted

from the Word2Vec model and applied to the rec-

ommendation problem, we also cite (Caselles-Dupr

´

e

et al., 2018). In this article, the authors indicate that

model hyper-parameters are important and have an

effect on recommendation quality. They show that

using different values for certain hyper-parameters,

leads to signiﬁcantly better performance for recom-

mendation tasks.

(Yang et al., 2022) proposed a global trajectory

ﬂow map and a Graph Enhanced Transformer model

(GETNext) that incorporates the global transition pat-

terns, user’s general preference, spatio-temporal con-

text, and time-aware category embeddings together

into a transformer model to make the prediction of

user’s future moves.

This paper proposes an innovative POI recom-

mendation method which has shown good experi-

mental results in terms of recommendation accuracy.

However, the complexity of the method and the need

for a large amount of trajectory data to train the model

may make its implementation in real-time systems

difﬁcult. Furthermore, it does not provide sufﬁcient

DATA 2023 - 12th International Conference on Data Science, Technology and Applications

248

detail on the selection of the model’s hyperparame-

ters, which may limit the understanding of the impact

of these parameters on the method’s performance.

(Liu et al., 2021) proposed a recommendation

model for the next POI based on a Gated Recurrent

Unit (GRU) neural network (RNN), which uses atten-

tion to learn to weight the representation of each POI

according to its category to improve recommendation

accuracy. However, the lack of detailed explanation

of the model’s design choices, such as the number of

units in the GRU or the number of layers in the neural

network, makes it difﬁcult to reproduce the proposed

model or adapt it to similar problems.

The solution we present in this paper is a trajec-

tory similarity based technique for next POI recom-

mendation. We calculate trajectory similarity using

classical metrics such as MRR and JACCARD, and

adapt them to Word2Vec embeddings, to assess the

beneﬁt of using embeddings over classical methods

for the next POI recommendation. Unlike (Grbovic

et al., 2015), we assume that the POIs aren’t prede-

ﬁned in the dataset, and use meshing technique to

identify them.

We provide recommendations of POIs that may

not be categorised in the same way but share simi-

lar semantic features, unlike (Liu et al., 2021), which

focuses on categorising POIs based on their type and

location.

We also carry out a study of the model hyper-

parameters to prove their importance and impact

on the quality recommendation as mentioned in

(Caselles-Dupr

´

e et al., 2018) but not detailed in (Yang

et al., 2022). This study allows us to position our-

selves with these works.

4 DATA PREPARATION

This study uses the YFCC dataset (Thomee et al.,

2016). We limits the dataset to France using its ge-

ographic boundaries as a bounding box. It is contain-

ing 2,052,004 records and 25 attributes. The choice

to apply our study to France is an arbitrary one, but it

does not affect the validity of the results, as the meth-

ods used are applicable to other countries or regions

of the world. This dataset does not have pre-deﬁned

Points of Interest (POIs), which relies on pre-deﬁned

POIs. User check-ins are used to identify POIs in-

stead, following a process where the space is divided

into a grid mesh with cells of a speciﬁc size in meters.

Each check-in is assigned to a cell that contains its

geo position, which is then assigned a unique number

corresponding to a POI. To ensure efﬁcient prediction

and achieve better quality, we aim to ﬁnd the optimal

grid granularity based on the required data density.

4.1 Effect of Grid Granularity on POIs

Density

We need a dense dataset. To achieve this, we ﬁrst

test the effect of the grid granularity on the POIs den-

sity, speciﬁcally the number of POIs, and the average

number of check-ins per POI based on the grid cell

size expressed in meters, as shown in Table 1.

Table 1: POI density based on grid granularity.

Cell size (m) #POIs avg #check-ins /poi

10 473 296 4

20 404 627 5

50 302 067 7

70 266 331 8

100 231 086 10

150 194 579 11

200 171 592 12

400 123 630 17

500 110 425 19

To have enough information to share for the col-

laborative ﬁltering task we are targeting, we need to

use a large number of check-ins. We therefore choose

a value of 10 check-ins per POI on average.

On Table 1, such density corresponds to a 100m

wide cell. This granularity generates a total number of

231,086 distinct POIs in France. Note that the value

of 100m is also used by (Lim et al., 2017) to map a

photo to a POI. Subsequently, we run all our experi-

ments with a grid granularity value of 100 m.

5 NEXT POI

RECOMMENDATION BASED

ON TRAJECTORY

SIMILARITY

As introduced in Section 2, next POI recommenda-

tion ﬁrst selects a set of candidate trajectories. Let T

be a set of trajectories and an input trajectory t that

ends with POI p. Let sim : (a, b) 7→ [0,1] be a similar-

ity function for a and b trajectories. S(t, p) is the list

of trajectories in T that contain p and have positive

similarity with t, ordered by decreasing similarity:

S(t, p) = [t

i

| t

i

∈ T, p ∈ t

i

,∀i < j,

sim(t

i

,t) ≥ sim(t

j

,t) > 0]

S

k

contains the top-k highest similar trajectories of S :

Embedding-Enhanced Similarity Metrics for Next POI Recommendation

249

S

k

(t, p) = {t

i

|t

i

∈ S(t, p),i ∈ [1,k]}

Then, each top-k similar trajectory ”votes” for the

next POI based on its similarity with t. Let next(s, p)

be the POI next to p in trajectory s. We assign a score

to each next(s, p) by aggregating the similarities be-

tween t and s:

score(r) =

∑

s∈S

k

, next(s,p)=r

sim(s,t)

The POI r with the highest score is recommended.

In the following, we investigate various similar-

ity functions with and without considering embed-

dings. This allows us to assess the relative beneﬁt of

embedding-based similarity functions applied to the

next POI recommendation.

5.1 Similarity Without Embeddings

The classical similarity metrics that are used in the

solution without embeddings are the following: JAC-

CARD and MRR.

JACCARD Similarity. Given two trajectories a

and b. The JACCARD similarity considers a and b

as sets of POIs, and is deﬁned as the ratio between

the length of the intersection of sets a and b, and the

length of the union of a and b :

jaccard(a,b) =

|a ∩ b|

|a ∪ b|

Note that when computing the similarity between a

candidate trajectory s and a test trajectory t, the last

POI of s is not taken into account because it is a can-

didate POI for the recommendation.

Thus, for JACCARD similarity we have

sim(s,t) = jaccard(s

′

,t) with s

′

being s without

its last POI.

MRR Similarity. The MRR similarity of two tra-

jectories, s and t, is inspired by the Mean Reciprocal

Rank function. It measures the rank of POIs that ap-

pear at a similar position in both s and t. The recip-

rocal rank is the multiplicative inverse of the rank. To

bring the higher score to POIs closer to the last POI

of t, t and s are traversed from the last POI, denoted

t[−1], to the ﬁrst one. Thus, t[−i] denotes the i

th

POI

of t in reverse order. MRR similarity is deﬁned by:

MRR(s,t) =

L

∑

i=1

1

i

R

i

(s,t) with

L = min(|t|,|s|)

R

i

(s,t) =

1 if t[−i] = s[−i]

0 else

5.2 Embedding Based Similarity

In this section, we have adapted the MRR and JAC-

CARD functions to support embeddings. To this end,

we train the Word2Vec model on user trajectories,

which exploits local context co-occurrence (neigh-

bor words). By associating a dense vector with

each word, Word2Vec captures context and identiﬁes

words (i.e., POIs) that share similar contexts if their

numerical vectors are close.

Then, we apply the next POI prediction algorithm,

based on the MRR cosine and JACCARD cosine sim-

ilarity metrics deﬁned below.

JACCARD Cosine Similarity. As described in al-

gorithm 1. The goal is to ﬁnd for every POI in test

trajectory t, the most similar POI in a train trajectory

s. The similarity between the two POIs is the cosine

of the corresponding vectors. Finally, the similarity

is the sum of all maximum similarities obtained for

every POI of t.

Algorithm 1: JACCARD cosine similarity function.

Require: s : train tr a j,t : test tra j,model

1: function JACCARDCOS SIMILARITY(s,t)

2: j ← 0

3: for p

1

∈ t do

4: v

1

← model.vector(p

1

)

5: m ← 0

6: for p

2

∈ s do

7: v

2

← model.vector(p

2

)

8: sim ← (v

1

· v

2

)/(||v

1

|| ∗ ||v

2

||)

9: m ← max(m,sim)

10: end for

11: j += m

12: end for

13: return j

14: end function

MRR Cosine Similarity. The goal is to compare

each POI of a test trajectory with every POI of a

candidate trajectory based on the cosine similarity of

their corresponding vectors. The matching score is

then the rank of the POI multiplied by the similarity

which captures a matching weight. In Algorithm 2 we

detail the MRR cosine similarity algorithm.

We provide readers with the code used for the so-

lution’s implementation which is available on

1

1

https://github.com/JarradSara/nextPOI-reco

DATA 2023 - 12th International Conference on Data Science, Technology and Applications

250

Algorithm 2: MRR cosine similarity function.

Require: s : train tr a j,t : test tra j,model

1: function MRRCOS SIMILARITY(s,t)

2: j ← 0

3: L ← min(length(s),length(t))

4: for i ∈ [1,L] do

5: v

1

← model.vector(t[-i])

6: m ← 0

7: for p

2

∈ s do

8: v

2

← model.vector(p

2

)

9: sim ← (v

1

· v

2

)/(||v

1

|| ∗ ||v

2

||)

10: m ← max(m,sim)

11: end for

12: j += m / i

13: end for

14: return j

15: end function

6 EXPERIMENTAL VALIDATION

6.1 Methodology

6.1.1 Train/Test Data

Test set Te and train set Tr are deﬁned as follows :

• For each couple of POIs (p

n

, p

r

), we gather all the

trajectories that end with (p

n

, p

r

). Then, we add

the most recent one to Te.

• We suppose that T represents all the trajectories

in the dataset. The train set Tr = T \ Te.

6.1.2 Baseline Algorithm

We compared our solution to a recommendation

method that uses a global transition matrix. To create

this matrix, we analyzed all pairs of successive points

of interest (POIs) visited in the training set and cal-

culated the frequency of transition between each pair.

When a user visits a POI, this method recommends

the POI with the highest frequency of transition from

the visited POI.

By using this baseline, we obtain a prediction

quality of 25.7% .

6.1.3 Experimental Methodology Followed

For our experiments, we use the dataset mentioned in

section 4 limited to France, which is in tabular format

with 25 attributes, and contains 231,086 unique POIs,

obtained by applying a mesh size of 100m.

The recommendation process shown in Figure 2

involves three steps: First, a Word2Vec model is

trained to generate embeddings for each POI from in-

put trajectories, which are then used in JACCARD co-

sine and MRR cosine metrics. Second, the prediction

algorithm from Section 5 is applied for the next POI

prediction. Third, the prediction quality is computed

using test trajectories. This is the ratio of correct pre-

dictions to the total number of test trajectories. Four

parameters affect the prediction quality: the similar-

ity function used, the number of similar trajectories

(k ∈ [1,50]), the dimension of the W2V embeddings

(vector size ∈ [2, 50]), and the number of learning it-

erations (epochs ∈ [5,100]). For a given combination

of these parameters, the relative beneﬁt of using em-

beddings is calculated as:

Beneﬁt = (

quality using embeddings

quality without using embeddings)

− 1).

6.2 Results and Discussion

Following the experimental protocol described in sec-

tion 6.1.3, our goal is to determine the optimal com-

bination of similarity metric, number of k-nearest

neighbors, and model hyperparameters that yield the

highest quality of recommendation. We ﬁrst provide

a brief summary of the numerical values obtained for

the maximum qualities and beneﬁts, along with the

optimal combinations to obtain them. We then pro-

vide detailed results for each experiment, varying the

mentioned parameters.

Using the JACCARD cosine in Table 2, we

achieve a maximum beneﬁt of 11.9% compared to

the JACCARD classic method, with corresponding

qualities of 33.37% and 29.8% respectively (a dif-

ference of 3.57). The optimal combination of values

that yields this beneﬁt is k=15, dimension=12, and

epoch=100.

Table 2: Max quality obtained with JACCARD metrics.

jaccard cosine jaccard Quality gap beneﬁt

33.37% 29.8% 3.57 11.9%

As for the MRR/MRR cosine method in Table 3,

the embedding-based MRR cosine method yields a

maximum beneﬁt of 7.4% compared to the non-

embedding MRR method, with corresponding qual-

ities of 33.08% and 30.8% , respectively (a differ-

ence of 2.28). The optimal combination of values

that yields this beneﬁt is k=20, dimension=30, and

epoch=100.

Table 3: Max quality obtained with MRR metrics.

mrr cosine mrr Quality gap beneﬁt

33.08% 30.8% 2.28 7.4%

Embedding-Enhanced Similarity Metrics for Next POI Recommendation

251

Figure 2: Functional architecture of the proposed solution.

The total execution time of all experiments (com-

putation of methods with/ without embeddings) is

∼20 minutes. The value of time execution was much

higher (∼2 hours) and which we were able to opti-

mise thanks to the distributed computation on Spark.

Details of all experiments are given below, in which

we compare the methods with and without embedding

according to different parameters.

6.2.1 JACCARD vs. JACCARD Cosine

We plot quality for embedding-based and non-

embedding-based JACCARD metric with different

values of k, epochs, and dimensions, and visualize

the results. The ﬁxed values for each experiment

were chosen based on the optimal combinations that

achieved the highest quality of prediction and beneﬁt.

Quality with Fixed K and Epochs, and Variable

Vector Dimensions Value. This experiment (Fig-

ure 3) concerns the qualities of JACCARD and JAC-

CARD cosine functions. We keep the value of k and

epochs ﬁxed at 15 and 100 respectively, and vary the

dimension of the Word2Vec model.

Figure 3: Quality of JACCARD/JACCARD cosine metrics

by dimension value.

Figure 3 shows that the quality obtained using

JACCARD (orange data points) is 29.8%, while us-

ing JACCARD cosine represented by the blue data

points, reach a quality of 33.37%, generating a ben-

eﬁt of 11.9% by using embeddings.

Quality with Fixed Dimension and Epochs, and

Variable K Value. For this experiment (Figure 4),

we keep the model dimension and epochs ﬁxed at 25

and 100 respectively. We then test the variation of the

values of k.

Figure 4: Quality of JACCARD/JACCARD cosine metrics

by k value.

Figure 4, shows that JACCARD, reach a quality

of 30.31%, whereas using JACCARD cosine, we ob-

tain a quality of 33.2%, generating a beneﬁt of 9.56%

using embedding.

Quality with Fixed Dimension and K, and Variable

Epoch Value. In the experiment shown in Figure 5,

we keep the dimension of the model and the value of

k ﬁxed at 25 and 20 respectively. We then test the

variation of the epoch values.

Figure 5: Quality of JACCARD/JACCARD cosine metrics

by epoch value.

DATA 2023 - 12th International Conference on Data Science, Technology and Applications

252

Figure 5 shows that the quality obtained with JAC-

CARD is 29.81%, while using JACCARD cosine, we

manage to reach a quality of 33.04%, generating a

beneﬁt of 10.83% by using embeddings.

6.2.2 MRR vs. MRR Cosine

Quality with Fixed K and Epochs, and Variable

Dimension Values. For this experiment (Figure 6),

we use the MRR and MRR cosine, keeping the val-

ues of k and epoch ﬁxed at 20 and 100, respectively.

We test the variation of the dimension values of the

model.

Figure 6: Quality of MRR/MRR cosine metrics by dimen-

sion value.

Based on Figure 6, the MRR achieves a quality of

30.8%. On the other hand, when using MRR cosine,

we obtain a quality of 33.08%. We note that the ben-

eﬁt generated by the use of embeddings is 7.4%.

Quality with Fixed Dimension and Epochs, and

Variable K Value. This experiment (Figure 7) con-

cern using the MRR and MRR cosine models, by

keeping the model dimension and epochs ﬁxed at 25

and 100 respectively, and vary k parameter.

Figure 7: Quality of MRR/MRR cosine metrics by k value.

In Figure 7, the quality obtained by MRR reaches

a maximum value of 30.9% for k > 15, whereas by

using MRR cosine we obtain a maximal quality of

32.2% for k = 20, which achieves a maximal beneﬁt

of 4.14% by using the embeddings. This also con-

ﬁrms that k, the number of neighbors, affects the qual-

ity of the results: taking into account up to 20 neigh-

bors tends to improve the recommendation quality.

Moreover, since the average number of similar

neighboring trajectories in the whole dataset is 37,

we can conclude that the optimal k parameter is about

twice lower than the average number of neighbors for

both methods with and without embeddings. Indeed,

it conﬁrms that considering as many neighbors as pos-

sible is not optimal.

Quality with Fixed Dimension and K, and Vari-

able Epoch Value. The experiment visualized in

Figure 8 concerns keeping the dimension of the model

and the value of k ﬁxed at 25 and 20 respectively, and

varying epoch values.

Figure 8: Quality of MRR/MRR cosine metrics by epoch

value.

In Figure 8, the quality obtained with MRR is

30.8%, while using the MRR cosine reach a quality of

32.75%, thus obtaining a beneﬁt of 6.03% with em-

beddings. We note that the data points representing

the MRR without embeddings are constant because k

is ﬁxed.

6.2.3 Effect of Mesh Conﬁguration on

Recommendation Quality

We vary the value of the grid cell size in the inter-

val [10,500] meters, and we compare the recommen-

dation quality with/without the use of embeddings

for all combinations of the following parameters: k

∈ [1,50], dimension ∈ [2,50], and epoch ∈ [5,100].

In Figure 9, we plot, for each grid cell size value, the

best quality obtained with embeddings and without

embedding.

As we can see, the best quality obtained with the

embeddings is equal to 67%, corresponding to a 10m

grid cell size. The quality without embeddings (in or-

ange) is 64%. For a cell size of 100m, we obtain the

Embedding-Enhanced Similarity Metrics for Next POI Recommendation

253

Figure 9: Quality of prediction according to grid cell size.

result seen in the previous sections, corresponding to

33% with the embeddings. On the other hand, for

larger grid cells, the quality drops to 27% for 500m.

We can conclude that the quality increase as the cell

size decreases, and that the methods with embeddings

are always performing better than the methods with-

out embeddings.

Using a grid cell size smaller than 100m results in

a better prediction quality of up to 67% for 10m wide

cells. However, with such small cell sizes, the dataset

is not dense enough in terms of the number of check-

ins per POI, and thus it does not meet the requirement

stated in section 4.1.

7 CONCLUSION

This paper demonstrates the effectiveness of using

embeddings with the Word2Vec model for the next

POI recommendation, highlighting that embeddings

provide a better recommendation quality than classi-

cal methods. Our contributions are :

• The POI identiﬁcation to handle check-in records

without POI information.

• The extention of JACCARD and MRR metrics

to embeddings, validating the beneﬁts of embed-

dings in terms of recommendation quality.

• The analysis of parameters that inﬂuence recom-

mendation quality.

Results show that embedding-based methods out-

perform classical methods for next POI prediction.

However, the study also notes some limitations of the

JACCARD and MRR metrics. The JACCARD metric

doesn’t consider the order in which POIs are visited

in trajectories, while MRR can be imprecise due to its

sequential nature, which takes into account the visit

order. To overcome these limitations, future work will

deﬁne other metrics that better address the problem

of similarity/distance between trajectories to achieve

better prediction accuracy.

In this study, we consider trajectories whith POIs

visited in a given order. However, this approach is not

always relevant. For example, tourists may visit sev-

eral museums in no particular order. This limitation

can affect the similarity measures between trajecto-

ries, which in turn affects the quality of the predic-

tion. An alternative approach that does not take into

account the order in the trajectory could strengthen

the validity and relevance of this study.

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