Design a Recommendation System in Real Estate Investment Based

on Context Approach

Tinh T. Nguyen

1,2

, Sang Vu

1,2,*

, Truc Nguyen

1,2

, Vuong T. Pham

2,3,4

and Hien D. Nguyen

1,2 a

University of Information Technology, Ho Chi Minh City, Vietnam

Vietnam National University, Ho Chi Minh City, Vietnam

University of Science, Ho Chi Minh City, Vietnam

Institute of Data Science and Artificial Intelligence, Sai Gon University, Ho Chi Minh City, Vietnam

Keywords: Recommendation System, Consultation, Many-Cold-Start-Users, Nearest-Neighbors Approach, Intelligent

System.

Abstract: The real estate investment industry has experienced a significant increase in user participation over the years,

with individuals keen on registering concurrent interests in both recent and prior projects. This growing trend

necessitates the development of an approach that can recommend real estate items in a simultaneous manner.

However, the presence of unrequired memberships and stop-by behaviors has introduced several challenges,

resulting in numerous cold-start scenarios for new users. This study proposes a recommendation system

tailored specifically for real estate, designed to offer warm-start item recommendations of cold-start users

using a content-based approach and a session-based recommendation system. Herein, a system for real estate

recommendation with acceptable warm-start item recommendations is proposed in the many-cold-start-users

scenario. The session-based recommendation system is adapted and made use of pre-existing methods to

effectively handle sequential and contextual data for the encoded attribute prediction of the next-interacted

item. Then, the nearest-neighbors method is employed weighted cosine similarity to identify conforming

candidates. The results demonstrate the effectiveness of efficiently integrating the information and the

difficulty in performing well in item recommendations simultaneously.

1 INTRODUCTION

Among the crucial challenges in any e-commerce is

maintaining the existing users while attracting new

ones and recommendation systems play a crucial role

in enhancing the user experience and improving the

overall functionality of a real estate investment

platform. The common recommendation system uses

historical records as prior knowledge to choose

candidates and performs most effectively with

adequate records (Roy and Duta, 2022, Nguyen et al,

2020). However, the recommendation task becomes

complex due to no clear data of new users which

inevitably leads to more problems for the

recommendation/consultation system. Therefore,

addressing this cold-start problem is essential for

enhancing the overall functionality and usability of

real estate investment platforms.

* Corresponding author.

https://orcid.org/0000-0002-8527-0602

The item cold-start problem occurs when a

recommendation system cannot recommend new items

due to record deficiencies and new listing omissions.

Cold-start items are new items with few or no

interactions (Wei et al., 2017), whereas the rest of the

items are warm-start items. As new items are added

continuously in practical applications, this problem can

cause missed opportunities for recommendations,

particularly in real estate recommendations wherein

users can register a concurrent interest in recent and

prior projects. Specific attributes of real estate, such as

location, developer brand, and living space, can

influence user behavior when searching and buying

properties (Chia et al, 2016). Thus, a recommendation

approach using these attributes to real-estate items

simultaneously must be determined.

Context information is useful for

recommendation tasks (Adomavicius and Tuzhilin,

Nguyen, T., Vu, S., Nguyen, T., Pham, V. and Nguyen, H.

Design a Recommendation System in Real Estate Investment Based on Context Approach.

DOI: 10.5220/0012210800003598

In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) - Volume 2: KEOD, pages 255-263

ISBN: 978-989-758-671-2; ISSN: 2184-3228

255

2011). This information is also significant for real

estate recommendation because user interests can

vary according to the context. For example, users

searching from urban areas may be more interested in

condominiums than users searching from rural areas.

Using context information is an approach to achieve

better real estate recommendations. The context

information is also represented by ontology (Bouihi

and Bahaj, 2018, Nguyen et al., 2021) and graph

information (Yan et al., 2019, Nguyen et al., 2023).

Deep learning techniques can be used to learn

item attributes and predict the representation of real-

estate items for the corresponding factorization

machine (Wei et al., 2017). Meta-learning can also be

applied (Vartak et al., 2017). However, these

approaches utilize the factorization machine, which

interprets the engagement or rating prediction to the

recommendation task. This requires user identifiers

and sufficient records for efficiency. Hence, this

characteristic is also observed in other real estate

search engines and e-commerce systems with

unrequired membership, which encourages the use of

a content-based approach and session-based

recommendation system.

A session-based recommendation system (Hidasi

et al., 2016) uses sequential behavior without relying

on user identifiers. This paper proposes a real estate

recommendation approach for solving the item cold-

start problem with acceptable warm-start item

recommendations in the cold-start-users scenario. It

modifies a session-based recommendation system

and employs existing mechanisms to efficiently deal

with sequential and context information for the next-

interacted item’s encoded attribute prediction. The

nearest-neighbors approach is also used with

weighted cosine similarity to determine conforming

candidates. The experimental results show that the

proposed method is not only among different applied

mechanisms but also against baselines using the top-

n recommendation with the real estate dataset.

2 PREMILARIES

2.1 Content-Based Recommendation

System

Content refers to the attributes of an item; this can

take the form of different data types, such as metadata

and text description. The content-based

recommendation system comprises a profile learner

and a filtering component when working with

structured item representations (Lops et al., 2011).

The profile learner predicts the user profile from

interacted item attributes in a similar representation

to the item profile, after which the filtering

component determines the relevant items using the

matching algorithm. As it relies only on item

attributes, it can constantly recommend cold-start

items. This study tends to use the nearest-neighbors

approach with weighted cosine similarity as a

filtering component. Weighted cosine similarity is

selected as the similarity function owing to its

efficiency and flexibility with our user and item

profiles, which are high-dimensional vectors.

Weighted cosine similarity is defined as follows:

(1)

iii

ii ii

wuv

Similarity

wu wv





where u

and v

are components of vector u and v,

resp., w

is the weight for both components.

2.2 Recurrent Recommendation

System Without User Identifiers

Without a user identifier, the task of recommendation

is underappreciated owing to the sparsity of training

data (Zhang et al., 2019). This sparsity leads the

recommendation system to learn from sequential

interactions without using user identifiers. Many

previous works (Li et al., 2017, Liu et al., 2018) relied

only on the sequence of interactions in each session.

Such a system is known as a session-based

recommendation system and uses RNN (Recurrent

Neural Network) as a core layer of the model owing

to its capability for capturing sequential patterns. The

system operates by receiving the click sequence of the

session, [𝑒



, 𝑒



, . . . , 𝑒



, 𝑒



] and predicting the next

click 𝑒



where 𝑒



is the i

event of the session. The

output of the system lists the scores for each item,

after which the system recommends only the top-n

highest-scored items to the user. The profile learner

utilizes this structure to predict the user profile from

the sequential patterns.

The process of splitting the click sequence into

training sequences working with the corresponding

structure is proposed in (Hidasi, 2016). Each training

sequence contains the input sequences and ground

truths. Input sequences use every possible prefix

within the training sequence and subsequent clicks as

ground truths (Figure 1). This generates an adequate

number of training sequences for deep learning.

2.3 Attention Mechanism in the

Current Recommendation System

A click sequence used in a recurrent recommendation

system is implicit feedback. It is indirect feedback

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

256

Figure 1: An example of splitting the click sequence into

training sequences.

implied from the user behavior and requires careful

consideration due to its characteristics of being very

noisy and providing no negative feedback (Hu et al.,

2008). It is impossible to determine whether the users

like or dislike the item on which they clicked, nor

whether a click is a miss click. The profile learner

uses the attention mechanism to deal with noise and

capture the purpose of the sequence, giving

precedence to each click differently.

Li et al. (2017) proposed the encoder portion of

the neural attentive recommendation machine

(NARM). NARM is an encoder-decoder session-

based recommendation system with an attention

mechanism. Its encoder portion incorporates two

encoders, the global encoder, and the local encoder.

The former represents the entirety of user behavior in

the click sequence, i.e., the last hidden state of the

RNN as follows:

= (2)

where, c

is the output of the global encoder and h

is the last hidden state of RNN. The local encoder

represents the main purpose of the click sequence,

defined as the sum of weighted hidden states from

every time step as follows (3):

= . (3)

ljj

cah



where, c

is the output of the local encoder, h

is the

hidden state of RNN at time step j and a

is the

weighted factor, which is defined as:

(, )

(4)

score h h



()

31 2

( , ) (5)

tj t j

score h h A A h A h

where, σ is an activation function, A

is a weighting

vector, and A

and A

are the learned weights of h

and

, respectively. As a result, both outputs from the

global and local encoders are concatenated and used

in the computation of the subsequent layers.

2.4 Application of Context Information

in the Recurrent Recommendation

System

Context information, such as the time and location of

the requested service, is useful when applied to the

recommendation task (Adomavicius and Tuzhilin,

2011). The profile learner uses the LC technique

(Beutel et al., 2018) to efficiently incorporate context

features, thereby overcoming difficulties inherent in

increasing the dimension of inputs that entail more

hidden units in the model. It works by determining

the elements-wise product of all embedded context

features in hidden states as follows:

()

1 * (6)

jjj

hwh=+



where h

is the hidden state of RNN at time step j and

is the embedded context feature.

The embedding layer of each context feature is

initialized by a 0-mean Gaussian distribution to

ensure that the multiplicative term has a mean of 1.

This initialization causes the multiplicative term to

act like an attention mechanism in the hidden state.

The element-wise product is performed both before

and after passing through the RNN. These

multiplications are considered as prefusion and post

fusion, consecutively.

3 CONTENT-BASED APPROACH

FOR RECOMMENDATION

SYSTEM IN REAL ESTATE

Using the content-based approach having a profile

learner and a filtering component, the item cold-start

problem has been solved. The profile learner is a

modified session-based recommendation system with

an attention mechanism to predict user profiles using

sequential and context information. The filtering

component uses the nearest-neighbors approach to

determine the most relevant items.

3.1 Profile Learner

The profile learner predicts a user profile composed of

the encoded attributes of the next-interacted item. It

utilizes the click sequence and context information.

Let [e

, e

, …, e

] denote a click sequence wherein 𝑒



Design a Recommendation System in Real Estate Investment Based on Context Approach

257

is the i th event of the sequence and [c

, c

, …, c

, e

n+1

]

are context features where 𝑐



corresponds to 𝑒



. The

profile learner predicts [f

, f

, …, f

m-1

, f

] where f

is the

encoded feature of 𝑒



determined from the click

sequence and context features. The encoded feature is

either one-hot or binary encoding depending on the

possible number of classes. Each f

prediction is either

a multiclass or binary classification depending on

ground truth encoding. For example, real estate

projects have the number of bedrooms as a feature.

This is reflected by one possible class among three:

one, two, or three bedrooms. Therefore, predicting this

feature is a multiclass classification problem.

Furthermore, it is a multilabel classification problem

when grouping such predictions as a feature

prediction. Particularly, we predict the possibilities of

all classes for each feature of the next-interacted item

used them as a user profile.

The structure of the session-based

recommendation system is modifed its task to the

objectives. Furthermore, the attention mechanism of

the encoder portion of NARM is used and adopted the

LC in the profile learner to efficiently deal with

sequential and context information, consecutively.

Thus, this efficiency should provide better user profile

prediction results and both warm-start and cold-start

item recommendations. Our profile learner received

an embedded item identifier, numerical features,

embedded categorical features, and the embedded

context features of a click sequence as inputs and then

predicted the user profile (Figure 2).

In the attention layer, global and local encoders are

used similar to the encoder portion of NARM but

using postfusion products instead of the original

hidden states. This replacement includes the effect of

context information when calculating the attention

score. The output of the attention layer is a

concatenated vector from the local and the global

encoders, which is used by the fully connected layers

to calculate the scores of the classes of all features.

Each fully connected layer is responsible for only one

encoded feature prediction; thus, its number of units is

equal to the number of corresponding classes. Its

activation function is either the softmax or sigmoid

function for multiclass and binary classification.

3.2 Filtering Components

The filtering component is responsible for

determining the candidates conforming to the

predicted user profile through the matching algorithm.

Herein, we used the nearest-neighbors approach to

gather top-n related items by calculating the scores of

all items using weighted cosine similarity, which

considers the numerical values of every possibility in

the user profile. It is also suitable for high-dimensional

vectors, which are similar to both our user and item

profiles. The representation of the item and user

profiles must be the same to compute the similarity

score. Thus, the concatenated vector of all encoded

features is used. Regarding these profiles, multiclass

and multilabel encoded features have different

influences on the similarity score calculation owing to

the different sum of values within the vector. To deal

with this, we selected weighted cosine similarity over

cosine similarity because it is flexible to assign

different weights to each component. Each 𝑤



for (1)

is defined as follows:

(7)







Where, n is the number of classes of the

corresponding multilabel feature. The structure of our

filtering component is shown in Figure 3. It was used

to calculate the similarity score between all item

profiles and the predicted user profile using (1) and (7)

together as the similarity function. This study designs

a method recommending the top-n items with the

highest similarity score to the user

3.3 Real-Estate Pricing System

A real estate pricing system is a tool or model used to

estimate the value or price of properties in the real

estate market. It utilizes various factors and data

points to provide insights into property valuation,

helping buyers, sellers, and real estate professionals

make informed decisions. A typical real estate pricing

system includes:

Data collection: A real estate pricing system

collects a wide range of data related to the properties

being analyzed. The system may also consider

additional data sources such as demographic

information, economic indicators, and infrastructure

development plans.

Feature selection: Once the data is collected, the

pricing system identifies the most relevant features

that significantly impact property prices. This process

involves statistical analysis and machine learning

techniques to determine which variables have the

strongest correlation with property values.

Model development: The pricing system utilizes

various statistical and machine learning models to

build a prediction model. Commonly used models

include multiple linear regression, random forests, or

neural networks. The choice of model depends on the

complexity of the data and the accuracy required for

price estimation.

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3.4 Regression Tree

A regression tree is a machine learning algorithm used

for solving regression problems. It is a type of decision

tree where each internal node performs a feature or an

attribute, and each leaf node represents a prediction or

an estimated value for the target variable.

Tthe process of building the regression tree involves

recursively partitioning the feature space based on the

values of different input features. The goal is to create

homogeneous regions or subsets of the data that share

similar characteristics in terms of the target variable.

This partitioning is done by selecting the feature and

the corresponding threshold that results in the best

split, often measured using metrics like mean squared

error (MSE) or mean absolute error (MAE).

3.5 Linear Regression

For the dataset of house prices and their corresponding

sizes, a linear regression model is built to predict the

price of a house based on its size, number of bed-

rooms, number of toilets, number of floors as Table 1.

Table 1: Some information of a house.

Orde

Price Area Bedrooms Toilets Floors

1 10 126 2 2 1

2 15 152 2 3 1

3 17 116 1 2 3

4 11 133 3 2 1

The linear relationship between the area, number

of bedrooms/toilets/floors of the house (variables 𝑥



𝑥



,𝑥



,𝑥



) and its price (dependent variable y) was

found. It can be represented by the equation:

y’ = f(x) = w

+ w

+ c (8)

where, w = {w

, w

} is the slope (regression

coefficient) and c is the intercept (y-intercept).

The linear regression model provides a linear

approximation of the relationship between house size

and price. It assumes a constant slope, indicating that

the price increases linearly with the size of the house.

However, it's important to note that linear regression

may not capture complex nonlinear relationships or

interactions between variables.

Figure 2: The structure of the proposed profile leaner.

Figure 3: The structure of the proposed filtering component.

Design a Recommendation System in Real Estate Investment Based on Context Approach

259

4 EXPERIMENTAL RESULTS

This section details the dataset and data preparation. It

also explains how the experiment was conducted and

evaluated to compare the performance of the proposed

approach and selected baselines.

4.1 Dataset and Data Preparation

The dataset used herein comprised one year of website

records captured in 2020 obtained from website

batdongsan.com.vn

, a popular real estate search

engine website, which included 13,425,274

interactions between 305,019 users and 6,849 items. It

contains 6,917 items with metadata that were used as

candidates; these metadata were consequently

processed as the item profile for each corresponding

item. It means each interaction has its context features.

First, the item profiles from its metadata are

provided the ground truth of the user profile prediction

and the participant in the similarity score calculation.

These metadata comprised 38 features, some of which

have missing and unique values. Features, which have

many unique values or missing values of more than

half, are removed. Those are not effective to interpret.

Besides, performing data imputation to fill the

remaining missing values based on other feature

values: the type of real estate, location, price level, etc.

Thereafter, all values are assigned into classes

depending on their corresponding feature

characteristic. Each value is assigned into a class with

an upper bound for discrete numerical features.

Values greater than or equal to this upper bound were

categorized equally. For example, the number of

bedrooms is a discrete numerical feature with five

classes: one, two, three, four, and five or more

bedrooms. This approach entailed the limitation of the

number of classes by grouping several sample classes.

After performing classifications, using either one-

hot or binary encoding encodes assigned class for one

possible class and many possible classes, respectively.

As a result, we generated item profiles composed of

15 encoded features for 6,917 items, the information

relating to which is shown in Table 2.

Finally, sets of sequences of context features is

divided into the training and testing sets. These

included context features for the prefusion. Seven

context features were used: n_click, requested device,

requested province, requested country, user agent,

page reference, and delta time. These features and

outline their participation are described in Table 3.

https://batdongsan.com.vn/

4.2 Evaluation

The top-n recommendation task is used for evaluation

because it is practical for real usage. This task

evaluates performance based on the recommendation

list provided by the system and the actual click of the

user. The appropriate metrics are Recall@K and

Mean Reciprocal Rank@K (MRR@K), where K is

the number of items in the recommendation list.

Recall@K measures the model performance whether

the actual click is on the K-items recommendation list.

𝑅𝑒𝑐𝑎𝑙𝑙@𝐾 







(9)

where, n

hit

is the number of cases having the actual

click and N is the number of all cases.

MRR@K measures the ranking performance of the

model as an average of reciprocal ranks of the actual

click within the recommendation list as follows:

𝑀𝑅𝑅@𝐾 





∑





 ∈

(10)

where, c is the actual click, C is a set of cases

having the actual click, N is the number of all cases.

The model performance is evaluated in terms of

two aspects: warm-start and cold-start item

recommendations. We assigned test cases to each

perspective using the type of their actual click item.

We defined new items appearing only in the testing set

and the top 100 most recently introduced items in the

training set.

Figure 4 is the results of items ranking in real-

estate dataset by their price for inputted requirements.

Figure 4: The results for ranking of items by their price.

4.3 Comparison Against Other

Methods

Baselines were categorized by their strengths in terms

of two aspects: warm-start and cold-start item

recommendations. There are warm-start item

baselines:

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

260

• S-Pop: A sequence popularity predictor that

recommends items ranked by their number of

interactions in the current sequence.

• Item-KNN (Sarwar et al., 2001): It uses the

nearest-neighbors approach with cosine

similarity between the recently interacted item

vector in the current sequence and other item

vectors to obtain the most relevant items.

• NARM (Li et al., 2017): An encoder-decoder

GRU-based session-based recommendation

system with an attention mechanism.

• STAMP (Liu et al, 2018): An MLP-based

session-based recommendation system with an

attention mechanism. It creates a

recommendation list based on sequential clicks.

The content-based approach (Lops e al., 2011)

with different profile learners and similar filtering

components for the cold-start item baselines. They

were as follows:

• CB (S-Pop): A sequence popularity predictor

that predicts the user profile using the item

profile of the most interacted item in the current

sequence.

• CB (Mean): A model that predicts the user

profile from the meaning of the item profiles in

the current sequence.

Table 2: Description, type, and post-processed information of the encoded attributes within the item profiles.

Table 3: Description and participation of context features in prefusion and postfusion.

Table 4: Comparison with warm-item test cases.

Method Recall@1 Recall@5 MRR@5 Recall@10 MRR@10 Recall@15 MRR@15 Recall@20 MRR@20

S-Pop 6.49 18.35 11.07 21.02 11.44 21.88 11.51 22.43 11.54

Item – KNN 8.95 25.64 14.8 36.56 16.25 43.49 16.8 48.49 17.08

NARM 13.75 32.79 20.62 43.39 21.93 49.91 22.45 54.59 22.71

STAMP 14.09 33.14 20.88 43.55 22.27 49.98 22.78 54.62 23.04

CB (S-Pop) 6.49 13.96 9.12 18.83 9.76 22.16 10.02 24.77 10.17

CB (Mean) 6.03 19.85 11.14 26.81 12.06 31.45 12.43 35.05 12.63

Proposed

approach

11.97 24.21 16.29 31.90 17.31 37.13 17.72 41.23 17.95

Design a Recommendation System in Real Estate Investment Based on Context Approach

261

Table 4 is the results of warm-start item

recommendation through 12,066 test cases.

Moreover, to identify conforming candidates, the

nearest-neighbors method is also combined with

weighted cosine similarity.

In terms of Recall@K, the proposed approach is

mostly fourth following Item-KNN, NARM, and

STAMP, respectively. The only exception in terms of

Recall@1 is that our approach is at the third place by

outperforming Item-KNN. In terms of MRR@K, our

approach is at the third place behind NARM and

STAMP, consecutively. Meanwhile, our approach

yields better performance in both terms compared to

cold-start item baselines.

These results are better in terms of ranking and

one-item recommendation. The proposed approach

cannot beat Item-KNN, NARM, and STAMP in

overall warm-start item recommendation owing to

two causes. The first is having more candidates.

There are 6,917 considered items when calculating

the similarity score, out of which not all participate in

the interaction logs. Conversely, these three baselines

consider only 6,105 items found in the training set.

The second is the disadvantage of using the only item

attributes to determine the candidates. This results in

retrieving only items like to the predicted user profile

while users can register their interests with different

attributes.

After weighing every feature equally when

computing the similarity score, this might not match

with user’s attributes priority. There could be user

specific requirements when searching for real estate.

Hence, making this approach more personalized by

incorporating different weights for each feature can

improve the recommendation performance.

5 CONCLUSION

In this paper, an approach for building a

recommendation system in real-estate is proposed. In

the case of numerous cold-start customers, this

method resolves the item cold-start problem with

respectable warm-start item recommendations. It

adapts a session-based recommendation system and

makes use of already in place methods to effectively

handle sequential and contextual data for the encoded

attribute prediction of the next interacted item. The

experimental results demonstrate that this method is

superior to baselines utilizing the top-n

recommendation with the dataset from the real estate

search engine as well as to other used methods.

Based on the idea, people in the same group

should react similarly to similar items, the

recommendation approach improves search results by

using customer demographic data (Matuszelański and

Kopczewska, 2022). In the future, the proposed

method will be combine the knowledge base of real-

estate investment (Nguyen et al., 2022) for

recommending more accuracy based on customers’

behaviors, which will be aimed at the demographic

profile of customer.

ACKNOWLEDGMENT

This research was supported by The VNUHCM-

University of Information Technology’s Scientific

Research Support Fund.

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