=Paper=
{{Paper
|id=Vol-3609/paper26
|storemode=property
|title=Design Approaches and Tools for the Implementation of a Medicinal Cocktails Recommendation System
|pdfUrl=https://ceur-ws.org/Vol-3609/paper20.pdf
|volume=Vol-3609
|authors=Sviatoslav Tyskyi,Solomiia Liaskovska,Andy T. Augousti
|dblpUrl=https://dblp.org/rec/conf/iddm/TyskyiLA23
}}
==Design Approaches and Tools for the Implementation of a Medicinal Cocktails Recommendation System==
https://ceur-ws.org/Vol-3609/paper20.pdf
Design Approaches and Tools for the Implementation of a
Medicinal Cocktails Recommendation System
Sviatoslav Tyskyi a*, Solomiia Liaskovskab, Andy T. Augoustic
a
Department of Artificial Intelligence, Lviv Polytechnic National University, Kniazia Romana Street, 5, Lviv,
79905, Ukraine
b
Department of Artificial Intelligence, Lviv Polytechnic National University, Kniazia Romana Street, 5, Lviv, 79905,
Ukraine
c
Faculty of Engineering, Computing and the Environment, Kingston University, Kingston, London, Room RV MB 215,
Main Building (RV), Roehampton Vale, United Kingdom
Abstract
The development of a recommendation system for medical cocktails is becoming
increasingly relevant in today's world, where health and well-being are becoming a higher
priority. Such a system allows patients to receive personalized recommendations for the use
of therapeutic beverages, contributing to the improvement of their health and quality of life.
The aim of the article is to create a Recommendation System that provides functionality for
convenient search of Modern Medicinal Cocktails recipes, based on the user's available
ingredients and his tastes. The object of the research is a system of recommendations for a
small sample of data and the application of this system. As a result of the research, a
Recommendation System was created that allows users to quickly and efficiently find a
recipe of Medicinal Cocktails with personalized health recommendations involves a deep
understanding of health and medical information, and it's important to ensure the advice
given is accurate and safe for users. The Recommendation System also provides personalized
recommendations based on the user's preferences. The obtained results confirm the
effectiveness of the proposed approach and allow us to recommend the use of the developed
Telegram bot for quick and convenient search of cocktail recipes with personalized
recommendations.
Keywords 1
Recommendation System, Medicinal Cocktails Recipes, personalized recommendations,
Python, data processing, data analysis, artificial intelligence.
1. Introduction
Many people use a Recommendation System to read the actual information about healthcare, life
being, entertainment and other useful services. At the same time all information about healthcare and
healthy lifestyle have become increasingly popular, especially among people with high activity life.
So a Recommendation System for Medicinal Cocktails will became popular among dietitians,
nutritionists and people how spent healthy life.
Developing a Recommendation System for Medicinal Cocktails recipes with intelligent
recommendations can have a significant impact on the health and life balance. In many countries, the
culture of preparing drinks for health that involves in person self-feeling, energy is very important,
and learning recipes and how to make cocktails is part of that culture.
Thanks to the Recommendation System, people should know more about Modern Medicinal
Cocktails culture and the right way to consume it. Recommendations and advice on the selection of
ingredients and preparation methods can help users understand how to properly enjoy cocktails
without harming their health and combine the ingredients correctly[1-3].
IDDMβ2023: 6th International Conference on Informatics & Data-Driven Medicine, November 17 - 19, 2023, Bratislava, SlovakiaEMAIL:
sviatoslav.tyskyi.mknssh.2023@lpnu.ua (A. 1); solomiya.y.lyaskovska@lpnu.ua (A. 2); augousti@kingston.ac.uk (A. 3)
ORCID: 0009-0006-7773-6560 (A. 1); 0000-0002-0822-0951 (A. 2); 0000-0003-3000-9332 (A. 3)
Β©οΈ 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
In addition, the Recommendation System can become a means of popularizing non-alcoholic
cocktails, which can be included in the culture of alcohol consumption and change people's attitude
towards alcoholic beverages[]. Increasing user awareness of soft drinks can help reduce alcohol
consumption and the production of alcoholic cocktails, thereby reducing the health effects of alcohol
and improving people's quality of life.
Therefore, it can be said that the development of such system has the potential to influence the
culture of drinking no alcoholic beverages, make this process more known and conscious, and provide
a healthier and more appropriate lifestyle.
The main purpose of this paper is to create a Telegram bot for cocktail recipes with intelligent
recommendations based on machine learning [4-6]. The implementation of such a product allows
users to quickly and efficiently find a cocktail recipe by name, ingredients or type of drink, as well as
receive personalized recommendations based on their search history and preferences. To achieve the
goal, the following tasks must be solved:
1. Collection and processing of cocktail recipe data, including name, ingredients, proportions,
description, and images [7,8].
2. Development of an algorithm for searching and filtering recipes by name, ingredients or type
of drink.
3. Implementation of a machine learning model to analyze the user's search history and
preferences and provide personalized recommendations [9-11].
4. Integration with Telegram API to provide interaction with users and give them access to work
functions.
5. Development of a user interface that ensures the convenience of interaction with the bot and
allows you to find a cocktail recipe, get recommendations, and save your favorite recipes.
6. Checking and adjusting the operation to ensure its stable and trouble-free operation.
7. Testing and improvement of work based on feedback received from users.
The object of study:
The object of the study is a Recommendation System for Medicinal Cocktails [10] with smart
recommendations.
The subject of study:
The subject of research is the development and implementation of machine learning algorithms for
the selection of personalized recommendations, as well as interaction with API and process
processing and input data analysis [11-14] to create an effective and convenient Telegram bot for
Medicinal Cocktails.
2. Content-Based Filtering
Content-Based Filtering (CBF) is one of the approaches to solving the problem of
recommendations [1]. This approach was designed to provide personalized recommendations based
on information about items that the user has liked in the past.
2.1. The mechanism of content-oriented filtering
The CBF method basically uses an item's description (or its "content") to recommend similar
items. So, for example, if a user liked certain movies with a specific actor in the past, the CBF system
will recommend other movies with that actor to the user.
Content-based filtering makes recommendations using keywords and attributes assigned to objects
in the database (such as products in an online marketplace) and matches them to a user's profile. A
user profile is created based on data obtained from user actions such as purchases, ratings (likes and
dislikes), downloads, searching for products on the website or adding them to the cart, and clicking on
links and products.
The basic concept behind CBF is that a user will like items that are similar to those he has already
rated highly in the past. Therefore, the main task of CBF is to identify items that are similar to those
that the user liked.
Similarity can be determined using similarity metrics [2], which are mathematical measures used
to determine how similar vectors are to each other. Different similarity metrics are used in different
contexts. Here are some of the main ones:
Euclidean distance: This metric defines the distance between two points in space. The smaller the
Euclidean distance, the more similar the objects.
π
βπ β πβ = ββ(ππ β ππ )2 (2.1)
π=1
where π = (π1 , π2 β¦ ππ ) , π = (π1 , π2 β¦ ππ ) - vectors.
Cosine similarity: These metric measures the angle between two vectors and is used to
determine similarity in a high-dimensional space. The closer the angle is to 0, the greater the
similarity.
π΄π΅ βππ=1 π΄π π΅π
πππ π = =
βπ΄ββπ΅β (2.2)
ββππ=1 π΄2π ββππ=1 π΅π2
where Π, Π β vectors.
Manhattan distance: This metric, also known as the L1-norm, measures the sum of the
absolute differences between the coordinates of two points.
π
π1 (π, π) = βπ β πβ1 = β|ππ β ππ | (2.3)
π=1
where π = (π1 , π2 β¦ ππ ) , π = (π1 , π2 β¦ ππ ) - vectors.
Jacquard measure: This metric is used to determine the similarity between two sets and
measures the size of the intersection of the sets divided by the size of the union of the sets.
|π΄ β© π΅|
π½(π΄, π΅) = (2.4)
|π΄ βͺ π΅|
where Π, Π β sets.
Pearson's metric: This metric is used to measure the statistical correlation between two
vectors. It takes values from -1 to 1, where 1 means full positive correlation, -1 means full negative
correlation, and 0 means no correlation.
βππ=1(π₯π β π₯Μ
)(π¦π β π¦
Μ
)
ππ₯π¦ = (2.5)
π π
ββπ=1(π₯π β π₯Μ
) βπ=1(π¦π β π¦Μ
)2
2
where π₯Μ
ΡΠ° π¦Μ
β sample averages.
ππ₯π¦ β [β1; 1]
2.1.1. Usage of content-oriented filtering
CBF is often used in recommender systems, such as movie, music, book, news, search engine
recommendations, etc. This approach can be very effective when detailed item descriptions are
available.
Once a user has performed a few searches, browsed a few items, or made a few purchases, the
content-based filtering system can start making relevant recommendations. This makes it ideal for
businesses that don't have a large user base to collect data from. It also works well for sellers who
have many users but few user interactions in specific categories or niches.
2.1.2. Advantages and disadvantages of content-oriented filtering
The recommendations correspond to the interests of the user. Recommender systems based on
content can be adapted to the interests of the user, including recommendations for niche products,
since the method is based on comparing the characteristics or attributes of the database object with the
user's profile. No input from other users is required to start issuing recommendations. Unlike
collaborative filtering, content-based filtering does not require input from other users to generate
recommendations.
For example, content-based filtering recognizes a user's specific preferences and tastes, such as hot
sauces made in Texas with organic Scotch Bonnet peppers, and recommends products with the same
attributes. Content-based filtering is also valuable for businesses with a large number of products of
the same type, such as smartphones, where recommendations must be based on many different
characteristics.
Recommendations are transparent to the user. Highly relevant recommendations create a sense of
openness for the user, strengthening their level of trust in the recommendations offered. For example,
in collaborative filtering, there are more cases where users do not understand why they see certain
recommendations. For example, let's say a user group that bought an umbrella also buys down coats.
Collaborative filtering creates a potential "cold start" situation where a new website or community
has few new users and lacks connections between users. So it is avoiding the "cold start" problem.
Although content-aware filtering requires some initial input from users to start making
recommendations, the quality of early recommendations is usually better than a collaborative system
that needs to add and correlate millions of data points before becoming optimized.
Content-based filtering systems are usually easier to create. The technical implementation of
creating a content-based filtering system is relatively simple compared to collaborative filtering
systems designed to simulate user-to-user recommendations.
On the other hand, CBF has several disadvantages. First, it is generally considered less accurate
than collaborative filtering methods. Second, it may miss some relevant items if they are not similar
enough to items already rated by the user. Third, it can lead to "personal tumor", when the system
recommends only very similar items, and the user does not get a new experience.
Given this, CBF may be a better choice in some scenarios, especially when detailed item
descriptions are available or when user interaction data is lacking. However, to maximize the
accuracy and variety of recommendations, CBF is often combined with other approaches, such as
collaborative filtering, in hybrid recommender systems.
2.2. Using Collaborative Filtering for creating a Recommendation system
Collaborative filtering is one of the widely used methods of recommender systems. This
technology is designed to solve the problems of information personalization in conditions of a large
amount of data, in particular, on the Internet.
2.2.1. The working mechanism of collaborative filtering
Collaborative filtering looks exclusively at historical interactions between customers and the
products they've used to recommend new products. The details of the element itself are not of great
importance, because all information about how the user interacts with this element is stored in a
special repository - the matrix of the user's interaction with the element.
Collaborative filtering is based on the idea that if two people have agreed on some issues in the
past, they are more likely to have the same opinion in the future. Technology can be divided into two
main types: user-based and item-based. User-based collaborative filtering looks for users who have
similar ratings to the current user and uses their ratings for recommendation. Item-based collaborative
filtering, on the other hand, analyzes the items that have been rated by the current user and looks for
items that are similar to those that the user has rated highly.
2.2.2. Usage of collaborative filtering
Collaborative filtering is widely used in various fields, including e-commerce, online streaming
services, social networks, and many others. For example, Amazon uses item-based collaborative
filtering to recommend products, and Netflix uses it to recommend movies and series.
Collaborative filtering works best when there is enough data about user behavior. It is also better
suited to situations where user interests change over time because it can adapt to those changes. If it is
important to take into account the interaction between users, collaborative filtering with its algorithms
can be a very useful tool.
2.2.3. Matrix factorization
Matrix factorization [3] is a popular approach in the field of Recommendation system, which is
commonly used in collaborative filtering. The main idea of this method is to decompose a large
matrix into two smaller matrices, which, when multiplied, will be as close as possible to the original
matrix.
For example, in the case of a recommender system, a large user-product matrix might be created,
where the rows correspond to users, the columns correspond to products, and the values in the matrix
represent the ratings that users have given to products. Since many users rated only a small fraction of
the products, this matrix is usually very sparse.
Matrix factorization decomposes this matrix into two smaller matrices: the user-factor matrix and
the product-factor matrix. Each row of the user-factor matrix represents a "factor profile" of the user,
and each row of the product-factor matrix represents a "factor profile" of the product. "Factors" here
are latent (or hidden) properties that are determined during the factorization process, and may
represent abstract concepts that reflect product properties and user interests.
The rating that the user gives to the product is modeled as a scalar product of the corresponding
factor profiles. This model is able to fill in the gaps in the original matrix by predicting scores for
user-product pairs that were not previously scored.
2.2.4. Advantages and disadvantages of collaborative filtering
There are some advantages and disadvantages ΠΎf using collaborative filtering. Letβs analyze the
advantages of collaborative filtering. Firstly it can help users discover new interests by recommending
new items similar to their interests. Also, it does not require detailed characteristics and contextual
data of products or items. All that is required is the user-item interaction matrix to train the matrix
factorization model.
There are some disadvantages: data sparsity can make it difficult to recommend new products or
users because recommendations are based on historical data and interactions. As the user base grows,
the algorithms face a performance problem due to the large amount of data and lack of scalability.
The second disadvantage is βLack of diversity in the long termβ. Because the algorithms work based
on historical ratings, they will not recommend items with little or limited data. Popular products will
become even more popular in the long run and there will be a lack of new options. Suffers from the
'cold start' problem is the third disadvantage that we analyzed.
2.3. Hybrid filtering and itβs using for the Recommendation system
Hybrid filtering was invented to overcome the limitations of individual recommender system
approaches, such as content-based and collaborative filtering. It combines the characteristics of both
approaches, trying to use their advantages and avoid their disadvantages.
2.3.1. Types of hybrid filtration
There are several types of hybrid [4] systems, depending on how they combine content-oriented
and collaborative approaches. For example, they may include weighted, overlapped, switched, mixed
or hybrid models. Letβs analyze each of type:
Combination method: In this method, content-oriented and collaborative predictions are generated
separately and then combined. This can be important if you have a large amount of data that can be
processed in parallel.
Fusion: In this method, the attributes obtained from each method are combined and used to support
recommendations. This can be useful if you want to combine the benefits of both methods.
Cascade method: One method is used to generate a ranked list of recommendations, and then a
second method is used to refine that list. This can be useful if the first method is able to generate a
rough list quickly, while the second method can take longer but provide more accurate
recommendations.
Feature method: One of the representations (content-oriented or collaborative) is used as input data
for the other. For example, a content-oriented system can be used to create a set of characteristics for
each user, which are then used in a collaborative manner.
Blended recommendations: Collaborative and content-aware filtering recommendations are simply
mixed together. This can be useful if you want to present a wide range of recommendations to users.
Switching: The system switches between collaborative filtering and content-oriented filtering
depending on the situation. For example, if the system has enough information about the user for
collaborative filtering, it can use this method. Otherwise, it can use content-based filtering.
Ensemble: In this method, content-based and collaborative predictions are combined in the model
training phase, usually using machine learning techniques such as decision trees, neural networks, or
regression.
2.3.2. Advantages and disadvantages of hybrid filtering
Letβs consider the advantages of hybrid filtering:
β’ Overcoming the "cold start" problem that occurs in the absence of user interactions.
β’ Ability to recommend more diverse items.
β’ Greater accuracy of recommendations compared to individual methods.
β’ Ability to work with different types of data.
The main disadvantages of hybrid filtering: complexity of implementation, as this approach
requires knowledge of various filtering methods; the possibility of overloading the system, since a
significant amount of data needs to be processed. The last disadvantage of hybrid filtering is loss of
transparency in the decision-making process, as different methods may have conflicting
recommendations.
3. The effective methods of implementing the system of recommendations
for Modern Medicinal Cocktails
The experimental part, in which filtering methods are applied to real data and the results obtained
are analyzed. The most effective methods of implementing the system of recommendations for
cocktails were determined, and their implementation was evaluated in the context of the specified
metrics.
3.1. Content-Based Filtering
Using Python and the Pandas library for data processing, a content-oriented recommender system
model was created. In this model, each object is represented by a vector characterizing its attributes.
The similarity metric is used to compare vectors of different objects. Two main similarity metrics
were implemented to compare their performance: Euclidean distance and sinusoidal similarity.
Using these metrics to determine the similarity between objects, recommendation lists were
obtained for each user and the recommendations were evaluated using the accuracy metrics RMSE,
MAE, MSE, MAPE, MRE (Table 1).
Table 1
Comparison of similarity metrics
Sinusoidal similarity Euclidean distance
MAE 0.7749 0.7725
MSE 0.9468 0.9471
RMSE 0.9730 0.9731
MAPE 0.2568 0.2567
MRE 0.002568 0.002567
Based on the accuracy metrics, sinusoidal similarity (2.2) was determined as the best. It will be
used in the future.
3.1.1. Collaborative filtering
After implementation and analysis of content-oriented filtering, experiments with collaborative
filtering were conducted. In this part of the study, matrix factorization was used to implement
collaborative filtering.
It was used to compare different matrix factorization algorithms, such as SVD, SVD++, NMF,
KNN Basic, KNN Means, KNN ZScore, CoClustering, to implement collaborative filtering and
compare their effectiveness. Each algorithm has its own characteristics and may be better suited for
certain types of data.
Using the RMSE and MAE evaluation metrics, the quality of the recommendations generated by
each algorithm was evaluated and the one that best fit the model was selected. After selecting the best
matrix factorization algorithm, it will be integrated with the content-oriented model to create a hybrid
recommender system.
In order to determine the best algorithm, it is first necessary to choose the hyperparameters so that
the algorithms give the best result.
Thus, for algorithms of the KNN family, it is necessary to choose the optimal value of the
parameter k. For this purpose, several values of the k parameter were selected and evaluated using the
RMSE metric and the MAE metric the optimal value of parameter k according to accuracy metrics is
30.
After the selection of hyperparameters, a comparison of matrix factorization algorithms was
carried out. The results are recorded in Table 2.
Table 2
Accuracy of matrix factorization algorithms
The Root Mean Squared Error Mean Absolute Error
(RMSE) (MAE)
CoClustering 1.211 0.9708
KNN Means 1.1453 0.9226
SVD 0.9953 0.8063
KNN Basic 1.0296 0.8357
SVDpp 1.008 0.8195
NMF 1.257 1.0167
KNN ZScore 1.1591 0.9345
SlopeOne 1.202 0.9572
For clarity, graphs were drawn comparing the accuracy of the algorithms using the RMSE metric
(Fig. 1) and the MAE metric (Fig. 2).
Figure 1: Graph comparing the accuracy of factorization algorithms using the RMSE metric
Figure 2: Graph comparing the accuracy of factorization algorithms using the MAE metric
So, from the above graphs, it is possible to determine the SVD algorithm as the most accurate
among matrix factorization algorithms. However, even for the SVD algorithm, the error is 0.99,
which can be lower due to the combination of collaborative and content-based filters.
3.1.2. Hybrid filtering
After individual implementation of content-oriented and collaborative filtering, both models were
combined to create a hybrid recommender system. A hybrid system combines the strengths of both
approaches and compensates for their weaknesses
In our case, content-based filtering was used to generate a base list of recommendations based on
the characteristics of products that the user had previously evaluated. Thanks to this, the relevance of
recommendations to the user's interests is ensured.
However, to ensure diversity of recommendations and take into account the behavior of other
users, collaborative filtering is additionally used. To determine the best combination of models, an
experiment was conducted in which the accuracy of all models was compared using the RMSE and
MAE metrics. The results of the comparison are shown in Table 3.
Table 3
Accuracy of matrix factorization algorithms
The Root Mean Squared Error Mean Absolute Error
(RMSE) (MAE)
CoClustering 0.6102 0.3060
KNN Means 0.6207 0.3131
SVD 0.6141 0.3099
KNN Basic 0.6192 0.3122
SVDpp 0.6243 0.3160
NMF 0.6260 0.3135
KNN ZScore 0.6208 0.3129
SlopeOne 0.6244 0.3143
For clarity, the graphs comparing the accuracy of the algorithms using the RMSE metric (Fig. 3)
and the MAE metric (Fig. 4) are plotted.
Figure 3: Graph comparing the accuracy of hybrid algorithms using metrics RMSE
Figure 4: Graph comparing the accuracy of hybrid algorithms using metrics MAE
As a result, recommendations are obtained that more accurately correspond to the user's interests,
and at the same time take into account the behavior of other users. The CoClustering algorithm with a
content-based filter turned out to be the most effective.
3.1.3. Conclusion
In the context of collaborative filtering, eight methods of matrix factorization were considered,
each of which presents its own characteristics and advantages. Regardless of the choice of a specific
method, collaborative filtering demonstrates high efficiency in the presence of a sufficient number of
user interactions.
A hybrid system that uses both methods has shown high efficiency, allowing to use the advantages
of both approaches and avoid their disadvantages. The hybrid model allows you to take into account
the interests of the user, the behavior of other users, and also provide various recommendations.
So, each of the considered approaches has its own strengths and weaknesses, but hybrid filters
performed best.
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