=Paper=
{{Paper
|id=Vol-3303/paper9
|storemode=property
|title=Extracting User Preferences and Personality from Text for Restaurant Recommendation
|pdfUrl=https://ceur-ws.org/Vol-3303/paper9.pdf
|volume=Vol-3303
|authors=Evripides Christodoulou,Andreas Gregoriades,Herodotos Herodotou,Maria Pampaka
|dblpUrl=https://dblp.org/rec/conf/recsys/ChristodoulouGH22a
}}
==Extracting User Preferences and Personality from Text for Restaurant Recommendation==
https://ceur-ws.org/Vol-3303/paper9.pdf
Extracting User Preferences and Personality from Text for
Restaurant Recommendation
Evripides Christodoulou1 , Andreas Gregoriades1 , Herodotos Herodotou1 and Maria Pampaka2
1
Cyprus University of Technology, Limassol, Cyprus
2
The University of Manchester, Manchester, UK
Abstract
Restaurant recommender systems are designed to support restaurant selection by assisting consumers with the information
overload problem. However, despite their promises, they have been criticized of insufficient performance. Recent research in
recommender systems has acknowledged the importance of personality in improving recommendation; however, limited
work exploited this aspect in the restaurant domain. Similarly, the importance of user preferences in food has been known
to improve recommendation but most systems explicitly ask the users for this information. In this paper, we explore the
influence of personality and user preference by utilizing text in consumers’ electronic word of mouth (eWOM) to predict the
probability of a user enjoying a restaurant he/she had not visited before. Food preferences are extracted though a trained
named-entity recognizer learned from a labelled dataset of foods, generated using a rule-based approach. The prediction of
user personality is achieved through a bi-directional transformer approach with a feed-forward classification layer, due to
its improved performance in similar problems over other machine learning models. The personality classification model
utilizes the textual information of reviews and predicts the personality of the author. Topic modelling is used to identify
additional features that characterize users’ preferences and restaurants properties. All aforementioned features are used
collectively to train an extreme gradient boosting tree model, which outputs the predicted user rating of restaurants. The
trained model is compared against popular recommendation techniques such as nonnegative matrix factorization and single
value decomposition.
Keywords
Consumer Personality, Food preference extraction, Recommender System, Topic Modelling
1. Introduction sonality and preferences, while explicit features refer to
ratings of restaurants, their estimated value, price, and
Dining is one of the top five tourist activities during a cuisine offered.
leisure trip that plays a central role in travel experience. There are several ways to extract user preferences for
Recently, interest on food experience has been growing restaurant recommendation [5]. The simplest is through
[1], with businesses in the hospitality sector seeking in- explicit queries by asking users to define their prefer-
sights regarding the dining behaviors and preferences of ences. This however has some disadvantages, as food
customers to improve decision making in areas such as preferences might not be covered by the questions asked.
marketing [2] and recommendation [3]. Past research Alternative methods utilize user ratings to find similar-
that utilizes food in recommender systems such as [4] ities between users and restaurants (e.g., collaborative
employ simple techniques such as frequencies of food filtering). Another method for preference extraction is
vocabularies in Bag of Words. However, such techniques user opinion analysis that utilizes natural language pro-
require a lexicon of complete list of foods that usually cessing.
is not available for different cousins and countries. In Traditional recommendation approaches base their rec-
this paper, we utilize implicit and explicit information ommendations on user preferences extracted from users’
of consumers’ eWOM to improve restaurant recommen- historical records, such as ratings, reviews, or purchases.
dation. Implicit information refers to textual comments Popular techniques include the collaborative and content-
in reviews that can be used to estimate consumers’ per- based filtering approaches. Recently, there is strong in-
terest in the utilization of users’ personality, since it is
ORSUM@ACM RecSys 2022: 5th Workshop on Online Recommender linked to perception, motivation, and preference, and
Systems and User Modeling, jointly with the 16th ACM Conference on is known to remain stable during adulthood. Personal-
Recommender Systems, September 23rd, 2022, Seattle, WA, USA
ity is directly associated with consumer emotions and
Envelope-Open ep.xristodoulou@edu.cut.ac.cy (E. Christodoulou);
andreas.gregoriades@cut.ac.cy (A. Gregoriades); has strong impact on satisfaction with theory indicating
herodotos.herodotou@cut.ac.cy (H. Herodotou); that people with the same personality have similar prefer-
maria.pampaka@manchester.ac.uk (M. Pampaka) ences and needs [6]. The application of users’ personality
Orcid 0000-0002-7422-1514 (A. Gregoriades); 0000-0002-8717-1691 has enhanced the performance of recommender systems
(H. Herodotou); 0000-0001-5481-1560 (M. Pampaka)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License in the tourism domain with results improving point of
CEUR
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Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
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ISSN 1613-0073
interest, destination recommendations, utilizing either
questionnaires or automated personality recognition. 2. Existing Knowledge
This paper illustrates the utilization of consumers’ per-
sonality and user preferences extracted from the textual This section provides a review of recommendation tech-
part of electronic word of mouth (eWOM) to improve niques, the concept of personality, and elaborates on how
recommendation. EWOM represent consumer opinions it has been used in recommender systems so far.
about products and services and has been used exten-
sively in identifying consumers’ preferences. Recom- 2.1. Restaurant Recommender Systems
mendations are made by training an Extreme Gradient
Boosting (XGBoost) prediction model using as features, Recommender systems aim to predict the satisfaction of a
the users’ personality and the users’ preferences (e.g., consumer with an item (product/service) he/she has not
food). XGBoost is used due to its good performance bought yet [11]. This is part of one-to-one marketing that
in similar recommendation problems [7]. The research seeks to match items to consumers’ preferences in con-
question addressed in this paper focuses on whether the trast to mass marketing aiming to satisfy a target market
integration of personality with other features inferred segment [12]. Popular approaches focus on consumers’
from structured and unstructured parts of online reviews past experiences (ratings) for the creation of a user-item
improves restaurant recommendation, in contrast to pop- matrix and based on that predict what is more appro-
ular model-based collaborative filtering (CF) techniques priate to a user depending on either similarity between
such as nonnegative matrix factorization (NMF) and sin- users or items (products, services) [11]. The relationship
gle value decomposition (SVD). between consumers or between products can be found
The proposed approach utilizes consumers’ food pref- using similarity metrics, and this method is known as
erences and personalities along with perceptions about Collaborative Filtering (CF) [13]. This has been success-
venues from eWOM to recommend most suitable restau- fully applied in tourism recommendation problems such
rants to tourists. Labelled personality data is utilized as hotels or points of interests, and is considered as one of
to train a BERT (Bidirectional Encoder Representations the most popular techniques [14]. Another popular tech-
from Transformers) classifier using the personality model nique is content-based filtering, that attempts to guess
of Myers-Briggs Type Indicator (MBTI) due to its good what a user may like based on items’ features rather than
results in previous studies [8]. User preferences are ex- their rating [13]. A hybrid approach takes the advantage
tracted through topic modelling and a trained food named of both content-based filtering and collaborative filtering
entity recognizer. An XGBoost model is generated to pre- [11].
dict the probability of a user liking an unvisited restau- CF techniques, however, suffer from the cold start prob-
rant based on its personality, preference, and themes that lem that occurs when very little or no data is available
characterize the venue. about a user and thus inability to identify similar con-
The research question addressed in this work is how sumers [15]. In addition, data sparsity exacerbates the
to best combine user preference and personality mod- problem when there are a lot of unrated items in the user-
els with topic features inferred from eWOM to produce item matrix. This occurs when there is not enough data
the best recommendation, in contrast to popular model- to populate the user-item matrix based on which to make
based collaborative filtering (CF) techniques. This is a reliable inferences [16]. In tourism, the collection of data
continuation of our previous work in [9, 10] that exam- is difficult and time-consuming due to the limited time
ine the use of personality and emotion in recommender that tourists spent at a destination. The cold start prob-
systems. The contribution of this work lies in the auto- lem appears with first-time users (tourists) since there are
mated detection of food preferences from eWOM and its no records of their purchasing activity at a specific des-
combination with user personality and topic modeling tination.To address these CF problems, recent methods
for restaurant recommendation. utilise machine learning techniques such as matrix fac-
The paper is organized as follows. The next section torisation to approximate the user-item matrix content
introduces background knowledge on restaurant recom- using latent variables that emerge from the initial data.
mender systems and techniques for extracting food pref- The singular value decomposition (SVD), optimized SVD
erences and personality from text. The next section de- (SVD++), and non-negative matrix factorization (NMF)
scribes techniques for identifying topics discussed in con- models factorize the user-item matrix and predict the
sumers’ eWOM and personality prediction using deep satisfaction of users for products that are unknown [17].
neural networks. Subsequent sections elaborate on the Alternatively, content-based approaches utilize metadata
methodology followed and the results obtained. The pa- about new products to address the cold start problem.
per concludes with the discussion and future directions. A useful source for obtaining these metadata is textual
information from eWOM and its analysis using text ana-
lytics [18]. An example application includes work by Sun
et al. [19] that improved CF performance by analysing
restaurants eWOM to define numerical features corre- mechanism for defining the most essential aspects of per-
sponding to consumers satisfaction through sentiment sonality that describes people characteristics that creates
analysis. In the same vein, topic modelling techniques and reflects their behaviour [28].
have been used with CF to assist in estimating the simi- Personality prediction is an important phase of
larity between consumers or items [20]. Finally, work by personality-aware recommender systems, and the two
Zhang et al. [21] used consumers or items characteristics main methods for doing so is through questionnaires and
to cluster them into groups, and then find correlations automated means. Generally, questionnaires are more
between clusters to address the data sparsity problem. accurate in assessing personality; however, the process
Recently, a strong interest emerged in using the per- is tedious while the automated approach is easier to con-
sonalities of consumers in an effort to better understand duct, by utilising user’s existing data that can be either
and match their needs, as “personality” relates to the text, images, videos, likes (behavioural data) etc. [18]
perceptions, feelings, motivations, and preferences of in- Predicting personality from text is a popular automated
dividuals [22]. The application of user personality has approach that is based on personality theory claiming
improved the performance of recommendations in the that words can reveal some psychological states and per-
tourism domain for points of interest compared to tradi- sonality of the author of the text. There are two main
tional methods [23]. Personality-based recommendations categories of techniques, the feature-based and the deep
have also been shown to greatly reduce the cold start and learning: the former uses unigrams/n-grams (open vo-
data sparsity problems, and improved the performance cabulary approach) or lexicons (closed vocabulary) of
of recommendations in areas such as online advertising, features relevant to personality, and the latter text em-
social media, books, and music [24]. However, these beddings learned from large corpus of text in an unsuper-
approaches do not take advantage of eWOM data from vised manner (language models). Popular feature-based
users on the web to extract their preferences and their methods utilize the Mairesse [29] and linguistic inquiry
personalities. They focus mainly on the extraction of and word count techniques [30]. Features from these are
user data from specialized questionnaires to collect con- fed into different machine learning classifiers (e.g., Naïve
sumers’ behaviours and personalities. Such approaches Bayes, support vector machines) to make predictions.
fail to continuously update the system because of the Obtaining such features however is a costly process and
time-consuming use of questionnaires that leads to the cannot effectively represent the original text semantics.
loss of automation and update limitations. To avoid feature engineering, deep neural models and
language models are employed to learn text representa-
2.2. Personality Extraction from Text tions that currently result in improved accuracy. Deep
models focus on the context of the text and not just a
Personality is a set of characteristics and behaviours of an static representation for a word or a sentence. Those kind
individual that influence many areas of his/her life such of deep learning techniques are using an attention mech-
as motivations, preferences, as well as consumer pref- anism that focuses on giving weights to words based on
erences and behaviour [23]. Applications of automated how they are used in a text giving the ability of captur-
personality predictions have been applied by researchers ing the semantic content [31]. A popular architecture is
on data from various social networks such as Facebook, the BERT (Bidirectional Encoder Representations from
Twitter, to explore correlations between personalities and Transformers) that utilizes transformers neural network
the different user activities, purchasing behaviors and architecture. Attention-based transformers have shown
liking of foods from specific cuisines [25]. that collecting the semantics of a text improves the per-
The two most popular text-based personality classifi- formance level and the predication accuracy of ML per-
cation methods are based on the Myers-Briggs Type Indi- sonality models [32]. Given this, the method proposed in
cator (MBTI) [26] and the Big Five [27] personality traits this paper utilizes attention-based personality prediction.
due to the availability of labelled data on these models. Most approaches use a binary classifier for each of
The classifiers with best performance are usually employ- the personality traits (MBTI) such as a classifier for
ing the MBTI personality model that focuses on 8 key extraversion-introversion etc. Such methods require pre
types of characteristics that people have, Extraversion or labeled data with the personality class. The first step in
Introversion, Sensing or Intuition, Thinking or Feeling, the process is the vectorization of the text into a form
and Judging or Perceiving, behaviours. The combina- that can be processed by ML algorithms [33]. This can be
tion of characteristics can shape 16 different personality done using open/closed lexicons or sentence embeddings
types and classify people to the proper personality clus- in the case of deep learning methods (BERT). The vector-
ter [13]. The Big 5 Personality model express personality ized data is used to train a classifier using the data label
in the following 5 dimensions: Agreeableness, Extraver- or fine tune a pretrained model in the case of BERT. The
sion, Openness to Experience, Conscientiousness, and trained and validated model can be used to predict unseen
Neuroticism. Such taxonomies are recognized as a valid data. Recent personality classification techniques that
utilize deep learning for Big Five personality prediction, tures (220) that resulted in the best model performance.
such as the DeepPerson [34] demonstrate classification The selected food features where then one-hot encoded
performance (AUC score) of around 70% per personal- for each consumer review. To identify the food prefer-
ity dimension, using different training datasets, which is ences of each user, reviews were grouped by user and the
much lower compared to classifiers that use MBTI data. most frequent food entities in each user’s reviews were
considered as food preferences. This process considers
that, when customers visit different restaurants and write
3. Technical Background comments about the food they ordered, irrespective of
the food’s quality and the review rating, it constitutes
The proposed method utilises a named entity recognition
food preference of the user.
to extract food preferences, an automated eWOM topic
modelling for the identification of themes discussed in
review’s text, a BERT-based personality classification, 3.2. EWOM Topic Modelling
and an ensemble tree-based regression for the prediction Topic modelling is a popular tool for extracting informa-
of consumer restaurant ratings. tion from unstructured data and is used in this work to
identify themes discussed by consumers in eWOM. Topic
3.1. User’s Food Preference Extraction models generally involve a statistical model aiming at
finding topics that occur in a collection of documents
A named entity recognition (NER) is utilized to extract
[36]. Two of the most popular techniques for topic analy-
the food preferences of customers. NER is a major com-
sis are the Latent Dirichlet Allocation and the Structural
ponent in NLP systems to extract information from un-
Topic Model (STM). In this study, the STM approach [37]
structured text. An entity can be any word or sentence
is used to develop a topic model due to its ability to incor-
that refers to the concept of question. There are two
porate reviews’ metadata such as sentiment (rating>3)
main approaches for the creation of a NER, the model-
that help with interpreting and naming the identified
based, and rule-based approach. The latter focuses on the
topics. Each topic in STM represents a set of words that
grammatical rules and linguistic terms to extract entities.
occur frequently together in a corpus and each document
The model-based approach generates machine learning
is associated with a probability distribution of topics per
models using a text with pre-labelled entities. Most food
document. The process for learning the topic model ini-
NER models as reported in [35] are trained on data that
tiates with data preprocessing that includes removal of
did not include Cyprus dishes thus their predictions were
common and custom stop-words and irrelevant informa-
insufficient for our case. There is a wide range of generic
tion (punctuation), followed by tokenization (breaking
libraries suitable for NER, such as NLTK, spaCy, Stan-
sentences into word tokens), and stemming (converting
ford NER, Stanza, and Flair, but none was able to provide
words to their root form). Initially, common stop-words
appropriate food recognition in text.
were considered and gradually with the refinement of the
To extract food preferences, the spacy library was uti-
model, additional stop-words that were irrelevant to our
lized, and several rules have been specified that enabled
goal were added to the list of custom stop-words such as
the extraction of sentences that mention food consump-
names of people, restaurants, cities, etc. The optimum
tion such as “I ate ”,“I had for dinner” etc. To generate
number of topics that best fits the dataset is identified
a sufficient training set, a local and international food
through an iterative process examining different values
recipe dataset was used. The returned sentences were
for the number of topics (K) and inspecting the seman-
annotated automatically based on the position in the
tic coherence, held out likelihood, and exclusivity of the
sentence where the food entity occurred. This was nec-
model at each iteration until a satisfactory model is pro-
essary in order to create a training dataset labelled with
duced [37]. Coherence measures the degree of semantic
food names, and their start/end position in the sentence,
similarity between high scoring words in the topic. Held
based on which the food NER training was performed.
out likelihood tests a trained topic model using a test set
The trained NER achieved an overall accuracy of 81%
that contains previously unseen documents. Exclusivity
(70/30 train-test split) and was applied on the restaurant
measures the extent to which top words in one topic
reviews to extract foods associated with each review. Due
are not top words in other topics. The naming of the
to the large number of food entities that were generated,
topics was performed manually based on domain knowl-
there were a lot of repetitions due to different spellings.
edge and the most prevalent words that characterize each
Thus, to reduce the dimensionality of the dataset, a fea-
topic.
ture selection process was performed using a random
forest machine learning model to identify the most im-
portant food names using the review ratings as the target
variable. The process yielded the optimum number of fea-
Figure 1: Overview of the approach and its evaluation
3.3. BERT Personality Classification has been used for personality prediction using the Per-
sonality Cafe MBTI dataset in [38] achieving an accuracy
Recent benefits of the “attention” mechanism in deep
of around 0.75. In contrast, other deep learning methods
learning models have demonstrated state-of-the-art per-
that use the Big 5 model as well as the popular stream-
formance in numerous text analysis tasks such as classi-
of-consciousness essay dataset such as the one reported
fication.
in [39] using CNN, achieve inferior classification perfor-
BERT uses a multi-layer bidirectional transformer en-
mance.
coder and is inspired by the concept of knowledge trans-
Despite their good results, BERT-based approaches
fer, since in many problems it is difficult to access suffi-
have been criticized that their best performance is re-
ciently large volume of labelled data to train deep models.
ported with short texts. Long text refer to text with
In transfer learning, a pre-train a model is learned from
more than 512 tokens. Such text however are compu-
massive unlabeled datasets not representing the target
tationally expensive to process thus most transformers
problem, but allows the learning of general knowledge.
models limit the number of tokens they can process si-
BERT-like approaches provide pretrained models and
multaneously. In our case, most reviews produced by
their embedded knowledge can be transferred to a tar-
consumers exceeded the 512 tokens limit and thus the
get domain where labelled data is limited. Fine-tuning
prediction of personality was considered as a long text
such models is performed using a labelled dataset repre-
classification problem. Different methods exist to dealing
senting the actual problem; these tune the model to the
with this issue, which include the naïve head-only, tail
task at hand. Fine-tuning adds a feedforward layer on
only or semi-naïve approaches, that either use the top
top of the pre-trained BERT. Previous work has demon-
number of words, bottom number of words, or combi-
strated that this pre-training and fine-tuning approach
nation of top/bottom/important words in the text. Such
outperforms existing text classification approaches. In
approaches lose information but have a minimum compu-
our case, fine-tuning the BERT model was performed
tational cost. Recent works have sought to alleviate the
using publicly available personality labelled data. BERT
computational cost constraint by applying more sophis- matrix is generated with rows corresponding to
ticated models to longer text instances such as dividing consumers and columns to restaurants. The cells
the long text into chunks and combining the embeddings of the matrix contain ratings when these are avail-
of the chunks. However, work by Sun et al. [40] that able since customers did not visit all restaurants;
investigated different long-text treatment methods for 3. Development of a topic model using as corpus
consumer reviews, showed that the best classification the eWOM (reviews) to identify consumers’ opin-
performance is achieved using naïve methods such as ions and how these are associated with each re-
using only the head or tail tokens of the text while drop- view. Restaurant’s topics are generated by aver-
ping all other content. In this work, we explore the naïve aging the topics theta values associated with each
and semi naïve methods to find the one with the best restaurant. This represents common consumer
personality classification performance prior to labelling opinions per restaurant;
users with their personality. The results, described in a 4. Assessment of customers’ personality from
subsequent section, show that the naïve approach yielded eWOM is achieved using the MBTI BERT per-
the best performance, which is in line with [40]. sonality classification model;
5. Food preferences of users are extracted from
3.4. XGBoost Regression eWOM’s text using a custom NER model;
6. The explicit information from each restaurant is
XGBoost regression is used in this study due to its ability
combined with implicit information that emerges
of producing good results in similar problems. It is an
from personality analysis, food preference, and
ensemble method; hence multiple trees are constructed
topic modelling. These features are used collec-
with the training of each tree depending on errors from
tively to enhance the user-item matrix and are
previous trees’ predictions. Gradient descent is used
used to train an Extreme Gradient Boosting (XG-
to generate new trees based on all previous trees while
boost) regressor model using as output variable
optimizing for loss and regularization. XGBoost regular-
the user rating of restaurants and taking values
ization component balances complexity of the learned
in the range [1-5]. The XGBoost is optimized us-
model against predictability. XGBoost optimization is
ing hyperparameter tuning and validated using
required to minimize model overfitting and treating data
train/test data split (70/30). The trained model is
imbalance, by tuning multiple hyperparameters. The op-
used to predict user ratings for restaurant users
timal values of hyperparameters can be determined with
have never visited;
different techniques such as the exhaustive (grid search),
Bayesian, or random. The grid search method combines 7. The performance of the XGBoost model is com-
all possible values of each parameter, to obtain the model pared against that of three popular model-based
with best performance, while the Bayesian utilizes results CF techniques, namely SVD, SVD++, and NMF.
from previous optimization cycles to identify hyperpa- The comparison models are trained using the ini-
rameters values with higher probability in improving the tial user-item matrix while the XGBoost using the
classifiers performance. Grid search is better but slower enhanced user-item matrix that includes explicit
while Bayesian is faster but not as accurate. In this work, and implicit information. The performance of
the grid search approach is adopted. the models is assessed using popular evaluation
metrics such as mean absolute error (MAE), mean
squared error (MSE), and root mean squared error
4. Methodology (RMSE).
The methodology employed to address our research ques-
tion is presented in Figure 1 and is implemented via the 5. Results
following steps.
The data collected includes 105k reviews written in En-
1. Collection of restaurant reviews from TripAdvi- glish from tourists who visited Cyprus between 2010
sor and extraction of consumers’ eWOM and ad- to 2020 and posted reviews about their experience with
ditional explicit information of restaurants such restaurants in Cyprus (publicly available). The total num-
as cuisine type, price range, and value for money; ber of unique users were 56800 and the number of restau-
2. Preprocessing of the data and preparation for sub- rants were 650. Figure 2 depicts descriptive statistics of
sequent analyses (topic modelling, personality reviews ratings per year. For this study only users with
classification). Preprocessing includes punctu- at least 20 reviews are considered and only restaurants
ations and URLs elimination, lowering of text, with at least 50 reviews yielding 93 unique users and 410
stop words removal, tokenization, stemming, and venues.
lemmatization. During this step, the user-item
Figure 2: Percentage of restaurant review ratings [1-5] per
year from 2010 to 2020
5.1. Learned Topic Model
To extract consumers’ discussed themes from eWOM,
an STM topic model was developed using the estimated
optimum K (30) number of topics based on the model’s
performance metrics in Figure 3, with focus on high co-
herence, high held-out likelihood, low residuals, and high
lower bound scores.
Figure 4: Average theta values per topic
5.2. Personality Labelling
The training of the binary classifiers was performed us-
ing the Personality Cafe MBTI dataset consisting of joint
user posts on a social network labeled by personality
type defined using MBTI questionnaire. The dataset is
publicly available on “Kaggle” [41]. To identify the BERT
long text approach with the best classification perfor-
mance, two techniques were examined, namely the naïve
and semi naïve approach and the one with the best per-
Figure 3: Topic performance measures for identifying the formance was used in the workflow. For the naïve ap-
optimum number of topics. The red circle indicated the K proach, we used the head-only using as sentence length
number of topics selected the 256 and 512 words and for the semi-naïve we used
chunking of text into 128 words and combining their
embedding. The results from this process, presented in
The naming of the topics in Table 1 was based on Table 2, showed that the 512-naïve-head approach outper-
domain knowledge, words with highest probability in formed the other approaches and thus it was employed in
each topic and words with high Lift score. Lift gives users’ personality classification. Results from the BERT
higher weight to words that appear less frequently in model outperformed personality models trained using
other topics. the same dataset and thus improved our confidence in
The probability distribution of topics per review de- the personality prediction of each user.
notes the probability of each topic discussed in a review The personality distributions in Figure 5 show descrip-
and the sum of all topics’ probabilities in each review tive statistics regarding the personalities of users accord-
total 1. Reviews are associated with the distribution of ing to the detected personality from the MBTI BERT clas-
topics prevalence per review. The trained STM model’s sifier fine-tuned using a labelled datasets and treating
theta values per review refer to the probability that a long text using the naïve-head approach with 512 tokens.
topic is associated with each review. These theta val- The acronyms refer to combination of dimensions of the
ues, shown in Figure 4, were used as features during the MBTI model. The trained BERT model predicts for each
training of the XGBoost model along with other features. dimension of the personality model the probability that
a user belongs to any of the personality traits (i.e., prob-
ability for Extraversion – Introversion (E/I), Sensing –
Table 1
Specified names for the topics that emerged from STM analysis
Topics Words with high probability and lift scores Topic Name
Topic1: great, really, music, live, day, atmosphere, enchiladas, music, really, live, pub Entertainment Atmosphere
Topic2: nice, prices, atmosphere, reasonable, big, family, polite, quick, nice, relaxing, Family Restaurant
families, cafe
Topic3: time, excellent, went, night, amazing, first, every, occasions, stay, went, amaze, Special Occasion
week
Topic4: eat, new, end, found, places, second, thai, always, second, none New Place
Topic5: lovely, recommend, highly, enjoyed, beautiful, setting, party, setting, party, Party Place
hosts, fabulous, thoroughly, absolutely
Topic6: well, lunch, local, attentive, wonderful, presented, chose, breaks, attentive, Lunch
presented, chose
Topic7: evening, bar, friends, though, group, customers, quiet, whiskies, though Evening/Bar
Topic8: restaurant, location, must, beach, view, right, perfect, definitely, must, far Location
Topic9: visit, will, back, really, worth, definitely, going, visit, called Worth Visiting
Topic10: wedding, amazing, even, similar, impression, organize, guest, events, beyond, Wedding Place
pleasure
Topic11: many, birthday, soon, booked, kitchen, also, october, good, love see, year this, Celebration parties
flight, travel, celebration
Topic12: experience, nothing, special, whole, maybe, dining, perfection, fiancée, maybe Not Worth
Topic13: restaurant, probably, also, mountains, open, available, well, best, more, owner, Out of town
managers, troodos
Topic14: summer, use, even, range, late, cool, evenings, use, dine, cozy Summer location
Topic15: always, can, class, owners, restaurant, number, first, classy, number, varied, Fabulous Place
feeling, interesting, hidden
Topic16: two, outside, can, inside, sit, world, get, disappointed, aircon, magic, noise, Outside eating
traffic, heat
Topic17: thai, tourist, across, gem, trying, partner, avoid, duck, again, overall, always, Asian Cuisine
bespoke, gimmicks, hardcore
Topic18: bit, little, better, average, like, quite, expensive, much, however, criticisms, Average Place
average,
Topic19: different, small, cheese, also, breakfast, euros, greek, platter, platter, options, Breakfast
vegetarian, bacon, eggs
Topic20: wife, return, disappointed, restaurant, reviews, favourite, holiday, isn’t, trip, Disappointment
done
Topic21: old, cypriot, road, stop, village, along, street, waitresses, road, walk Stop during trips
Topic22: busy, get, people, table, lot, need, without, joyful, early, book Busy Place
Topic23: years, restaurant, made, visiting, coming, several, since, ago, forward, visits Visit over years
Topic24: staff, friendly, always, see, come, welcoming, feel, chat, truly, smile, come Welcoming Staff
Topic25: chips, served, priced, set, large, course, portion, adults, chips, portion, reason- Good portions
ably
Topic26: best, cyprus, don’t, ever, never, restaurants, know, traditional, meze Traditional foods
Topic27: value, money, recommended, excellent, variety, high, meals , bringing, best, Value for money
cyprus, eaten
Topic28: couldn’t, enough, friend, eat, fresh, away, wow, take, out, basilica, excellent, Fresh ingredients
lovely, rice, more, time ,highly
Topic29: ordered, came, table, order, waiter, asked, arrived, minutes, waited, waitress, Bad service
left, seated, orders, bill
Topic30: just, restaurant, basic, way, like, standard, much, full, unacceptable, much, Nothing Special
restaurant
Intuition (S/N), Thinking – Feeling (T/F), and Judging – which is depicted in Figure 5. The BERT model deals
Perceiving (J/P)). Combinations of letter from each cate- with 4 classifiers, one for each of the dimensions above.
gory generate 16 four-letter personality types: ISFJ, INFP, The classifier’s average area under the curve (AUC) per-
INFJ, ISTP, ISTJ, ISFP, INTP, INTJ, ENTP, ESFP, ENFP, formance is 87%. This is an improved performance com-
ESFJ, ESTP, ESTJ, ENFJ and ENTJ, the distribution of pared to alternative personality classification techniques
Table 2 personality-based approaches over these baseline mod-
Performance results per long text treatment els. The traditional techniques were also optimized by
tuning two hyperparameters, the number of factors and
Long text treatment for MBTI BERT AUC ACC
the regularization value.
Naïve - head 512 tokens 0.878 0.839 In the experiments conducted using the aforemen-
Naïve - head 256 tokens 0.784 0.759 tioned restaurants reviews, the data was initially split
Semi naïve - Sliced text 128 tokens 0.653 0.662 into test and training sets (70/30) using stratified sam-
pling to guarantee that all user ratings are sufficiently
represented in the test and training samples. The models
that utilize deep learning and Big Five personality model, were hyper tuned, trained, and tested using the same
such as the DeepPerson [34] that achieved AUC of around samples. The aforementioned metrics were computed,
70%. and the results that emerged (see Table 3) show that the
MBTI XGBoost model produced the best performance
among all other models. Both personality-based models
outperformed traditional approaches, which indicates
that the use of personality and eWOM-extracted topics
improved the recommendations.
Table 3
Performance results per model incorporating all features
Performance metric SVD SVD++ NFM XGB
(lower is better)
Mean Absolute Error (MAE) 0.65 0.68 0.82 0.40
Mean Squared Error (MSE) 0.87 0.89 1.22 0.24
Root Mean Squared Error 0.93 0.94 1.10 0.49
(RMSE)
Figure 5: Distribution of MBTI personality traits using each 6. Conclusions
dimension’s acronyms
This study proposes a combined user-preference with
user personality restaurant recommendation approach
and constitutes one of the first studies that use customer
5.3. Training and Evaluating the XGBoost preference along with personality in the restaurant rec-
ommendation problem. It utilizes a popular personality
Models model (MBTI) to enhance the restaurant recommenda-
The enhanced user-item matrix that emerged from the tion process by fine tuning a BERT classification model
personality model, food preferences, and the topics asso- on personality labelled dataset. Due to the length of
ciations per user and venue were used to train an XGB the training data, the best long-text handling approach
regressor model. (naïve-head 512) was employed during BERT model tun-
The XGBoost model underwent hyperparameter tun- ing. EWOM themes are extracted through topic mod-
ing prior to training by tuning the models’ learning rate, elling from eWOM’s text and are also used as additional
gamma, subsample, and regularization options using grid features of restaurants and users that refer to implicit
search. Traditional recommendation models, namely preferences of users and properties of restaurants. All
SVD, SVD++, and NMF were generated using the sur- aforementioned features are used collectively to train an
prise python library. The models were compared based XGBoost regressor to predict consumers’ satisfaction (i.e.,
on the following performance metrics: the mean abso- rating) for unvisited restaurants. The results show that
lute error (MAE) that represents the average of the ab- the MBTI model in combination with topics from eWOM
solute difference between the real and predicted values, outperforms the model-based collaborative filtering tech-
Mean Squared Error (MSE) and Root Mean Squared Er- niques, offering a first indication that the application of
ror (RMSE) that is the square root of MSE. Comparison personality and food preferences in restaurant recom-
of the two models against traditional recommendation mendation can have valuable results. Future work will
techniques revealed an improved performance of the focus on evaluating additional long-text handling tech-
niques and combine the results of the learned classifiers formation Systems and Technologies, Springer In-
with other traditional machine learning models in an en- ternational Publishing, Cham, 2022, pp. 13–21.
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