Journal of Information Technology and Computer Science Volume 6, Number 1, April 2021, pp. 107-116
Journal Homepage: www.jitecs.ub.ac.id
Application Of A Hybrid Method To Build A Mobile
Device-based Event Recommendation System
Dio Saputra Kudori*1, Herman Tolle
2, Fitra A. Bachtiar
3
1,2,3Faculty of Computer Science, Brawijaya University {[email protected], [email protected], [email protected]}
*Corresponding Author
Received 15 July 2020; accepted 14 June 2021
Abstract. In everyday life there are many events are held. These events use various ways in announcing the event for attracting people to participate come in the event. Because there are many events that are held in everyday life, an event
recommendation system can be implemented to provide event recommendations that are appropriate for the user. In developing event recommendation systems, there are many methods that can be used, the one that frequently used is
collaborative filtering. The event recommendation system has a unique character compared to other recommendation systems. This is because the event
recommendation system does not use the classic scenario of a recommendation
system. In this study we tried to use a hybrid method that combines collaborative
filtering with sentiment analysis. The experiment show that the results of the event recommendations have an accuracy value of 82%. It shows that the hybrid method can be utilized for developing event recommendation systems.
Keyword: event, sentiment, accuracy, filtering
1 Introduction
Recommendation systems are software and techniques that provide advice or
suggestion on certain items to be used by users. In recent years, the recommendation
system has become very popular and has become an important part of various
marketplace site, social media, entertainment, and even search sites that are often used
by the public. One type of recommendation system that is currently popular is the event
recommendation system. According to Any Noor [1], an event is an activity held to
celebrate important things throughout human life, either individually or in groups that
are bound by customs, culture, tradition, and religion which is held for specific purposes
and involves the community environment which is held at any given time. While the
event recommendation system is a recommendation system that has an output in the
form of suggestions regarding events that are in accordance with user preferences. At
present, there are very many events taking place in one place at the same time. For
example in Malang, Indonesia during the month of April 2018, there were around 494
events. The events include education (for example: 10th National Student Scientific
Writing Competition (KATULISTIWA)), culinary (for example: Malang One Million
Coffee 2018), sports (for example: 2nd Cakra Run 'Be The Fastest') and others. Besides
having a large amount, these events have different themes. With so many selection of
events being published either through the website, social media and other media, users
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will have difficulty finding events that are in accordance with their preferences.
Therefore, there is a need to develop a system that can provide event recommendations
that are in accordance with user preferences.
The event recommendation system has a unique character compared to other
recommendation systems. This is because the event recommendation system does not
use the classic scenario of a recommendation system (for example: the film
recommendation system), where items to be recommended have been ranked by other
users. In the case of the event recommendation system, the items to be recommended
are definitely not yet rated or rated by other users. This is because the event has not yet
taken place. If the event has already been carried out, then the event cannot be included
as a recommendation. The uniqueness of the event recommendation system causes the
event recommendation system cannot be solved using traditional collaborative-filtering
algorithms like other recommendation systems [6]. collaborative-filtering has
advantages because it does not require knowledge domains [8] collaborative-filtering
is suitable to be applied in problems that have a high level of difficulty in analyzing
content, such as music and film recommendations. collaborative-filtering have a
difficulty of making recommendations when the users or the items are new. This
problem is usually called a cold-start problem [7]
Previous studies have tried to implement recommendation system of an events
using various techniques. usually the hybrid method is used to solve the cold-start
problem, hybrid method is a combination of methods . There are study that implement
combination of collaborative-filtering and content-based to developing event
recommendation system that study integrating social networking site service and data
scrapper to supply the required data to develop event recommender system [4]. another
study using combination of item tag base and user knowledge base. it store item tag
information according to the user preferences and store user personal information as
required data for developing event recommender system [5].
In this study a hybrid method is proposed to overcome the problem specified
above. because from previous study shows that developing event recommender system
cannot using traditional collaborative-filtering algorithm. a hybrid method that used in
this study is a combination of collaborative-filtering and sentiment analysis,
collaborative-filtering is used to predict user rating of event based on another user rating
of selected event. sentiment analysis will be used to add value to user predicted rating
based on sentiment polarity score that occurred from selected event comment. 2 Previous Study Several method can be applied to developing recommender system, collaborative
filtering and content-based filtering are the most frequently used method. For event
recommendation system there are some method that used in previous study. The
previous study [4] combine collaborative filtering method and content-based method to
developing event recommendation system. The study integrating data from social
networking sites services and data collection scrappers, it use user’s friends preferences
from social networking sites to give recommendation. Every event recommended to a
user is displayed along the information of to which friends of the user the event is also
recommended. This may be as important as the date and time or location of an event.
This reveals the possible companies the user may choose to go with to the event being
recommended [4].
Another study use combination of item tag base (ITB) method and user
knowledge base (UKB) method to developing event recommendation system [5]. ITB
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stores the items to be recommended to the user and UKB stores personal information
of the users as well as their preferences. The results obtained from that study is
satisfactory, with 99.3% of the responses were ranked with 3 or more point (1-5 point
available) and 0% of responses correspond to a minimal score (1 point).
Another study proposed a novel event scoring algorithm called reverse random
walk with restart to obtain the user–event recommendation matrix [10]. in that study,
they first construct a heterogeneous graph to represent the interactions among different
types of entities in an event-based social network. the even recommendation is
considering global event capacity and local user preference.
Most of previous studies is using hybrid method to build an event
recommendation system. In this study also uses the hybrid method in building an event
recommendation system, but the method used is a combined method of collaborative
filtering and sentiment analysis, where the collaborative filtering method will be used
to predict user ratings for an event while sentiment analysis will be used to calculate
the sentiment polarity score of the event and add the sentiment polarity score with
predicted user rating. comments on an event.
3 Methodology
The proposed method of Hybrid Filtering through several steps to get a event
recommendation. An overview of the proposed method can be seen in Figure 1.
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Figure 1 Flowchart of Proposed method step
There are two main steps in the proposed method. the first step is calculate prediction
rating of user using collaborative filtering method the second step is calculate sentiment
polarity score of event that was predicted. the last step is calculate final score that
obtained from combining predicted rating score with sentiment polarity score. if the
score is above the threshold the event will be recommended.
3.1 Data Source
In this research, data was obtained by creating a social media application specifically
for managing events, where users can upload information about the event to be held.
Users can also follow the event organizer account to get info related to the events shared
by the account. Interactions that users can do with shared events are like, comment and
rate. The user interaction will be used by the system to become calculation data in
determining event recommendations. Data sets are taken within a period of one month
from 1 february 2020 until 1 march 2020 . Obtained data during the collection period
can be seen in the Table 1.
Tabel 1 Data of Events
No Event Name
1 Malang Tempoe Doeloe - Uklam Uklam Heritage
2 Kickfest XIII
3 Malang Flower Carnival
4 Festival Mbois 4 5 Urban Jazzy Festival
6 Malang Fashion Festival
7 Kampung Cemplung Festival
8 Wisata Edukasi Museum Brawijaya 9 Pamungkas The End Of Flying Solo Era
10 Tur Bayangan Hindia-Lomba Sihir
11 Online #Happyconcert With Ardhito Pramono
12 Patjar Merah 13 Islamic Book Fair #36 Malang
14 Malang Emotional Healing Bersama Adjie Santosoputro
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No Event Name
15 Product Photography Menggunakan Smartphone 16 Car Free Day Malang
17 Jackcloth Goes To Malang
18 Workshop Hypnosis & Hypnotherapy
19 Phum Viphurit Live Virtual Concert 20 The Make Up Workshop Glowing Look
3.2 Hybrid Method
In this research, we proposed a hybrid method for developing event recommendation
system. Hybrid method that we proposed is a combination of the user-based
collaborative filtering and sentiment analysis. User-based collaborative filtering used
for predict user rating while sentiment analysis used for adding value of user rating
prediction, the value. The proposed hybrid method is shown in Figure 2.
Figure 2 Flowchart of Event Recommendation System
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3.2.1 Collaborative Filtering
Collaborative Filtering (CF) is the process of filtering or evaluating items using the
opinions of others. The main idea is to dig up information about past behavior or
opinions from a user community which is then used to predict which items will be liked
to a user.
There are three assumptions idea in Collaborative Filtering, people have
similar interest and preferences, the user preferences and interests are stable, prediction
of user choice can be done by using their past preferences [1]. The collaborative
filtering algorithm also used other user’s preferences to compare with user’s
preferences and find the nearest neighbors because the user choice can be influenced
by user community.
The first step of collaborative filtering algorithm is to obtain the users history
profile, which can be represented as a ratings matrix with each entry the rate of a user
given to an item [2]. A ratings matrix consists of a table where each row represents a
user, each column represents a specific movie, and the number at the intersection of a
row and a column represents the user’s rating value. The absence of a rating score at
this intersection indicates that user has not yet rated the item. Owing to the existence
problem of sparse scoring, we use the list to replace the matrix.
The second step is to calculate the similarity between users and find their
nearest neighbors. There are many similarity measure methods. The pearson correlation
coefficient is the most widely used and served as a benchmark for CF. Generally we
use the Cosine similarity measure method, it’s calculate equation as follows:
𝑠𝑖𝑚(𝑥, 𝑦) =𝑐𝑜𝑠 𝑐𝑜𝑠 (�⃗�, �⃗�) =∑𝑠∈𝑆𝑥𝑦
𝑟𝑥,𝑠𝑟𝑦,𝑠
√∑𝑆∈𝑆𝑥𝑦𝑟𝑥,𝑠
2 √∑𝑆∈𝑆𝑥𝑦𝑟𝑦,𝑠
2 (2)
Where 𝑟𝑥 is rating of user 𝑥 on item 𝑠 and 𝑟𝑦 is rating of user 𝑦 on item 𝑠, 𝑆𝑥𝑦
indicates the items that user 𝑥 and 𝑦 co-evaluated.
The last step is to predict the items rating. The rating is computed by a
weighted average of the ratings by the neighbors [2].
𝑘 =1
∑ 𝑠𝑖𝑚(𝑥,𝑦) (3)
𝑟𝑐,𝑠 = 𝑘 ∑𝑐′∈�̂� 𝑠𝑖𝑚(𝑐, 𝑐′) × 𝑟𝑐′,𝑠 (4)
𝑟𝑐,𝑠 is item 𝑠 rating by user, 𝑐 is user, 𝑐’ is other user, 𝑟𝑐′,𝑠 is item 𝑠 rating by
other user.
3.2.2 Sentiment Analysis
Sentiment Analysis (SA) is a method that identifies the sentiment expressed in a text
then analyzes it. Therefore, the target of SA is to find opinions, identify the sentiments
they express, and then classify their polarity. The sentiment will be separated in three
class: positive, neutral, and negative. Positive class represent good user’s opinion,
Neutral class represent neither good nor not good user’s opinion, and Negative class
represent not good user’s opinion. The data sets used in SA are an important issue in
this field. The main sources of data are from the product reviews. These reviews are
important to the business holders as they can take business decisions according to the
analysis results of user’s opinions about their products.
For implementing SA, it’s need to have database of each class words: positive
words database, neutral words database, and negative words database. Moreover it’s
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also need database of ignore list word, the ignore list word will remove words that
doesn’t represent user’s sentiment. The process of SA on product reviews shown in
Figure 1.
Figure 3 Sentiment analysis process on product reviews
The result of implementing SA is classify user’s opinion and scoring it.
3.2.3 Final Recommendation
In this research, final recommendation obtained by combining user-based collaborative
filtering prediction rating with sentiment score from sentiment analysis. User-based
collaborative filtering calculate user rating prediction from user preferences while
sentiment analysis calculate sentiment score from another user’s comment in an event.
Figure 3 Output of used method
Figure 3 explain the output of user-based collaborative filtering and sentiment
analysis, each method has different input and output. The final result of user rating
prediction is the result of the adding user rating prediction with sentiment score. With
a change in value of user rating prediction, the result of recommendation will be
different.
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3.3 Evaluation Method
In this research, accuracy testing is used to evaluate the result of recommendation.
Accuracy value obtained by using formula 1.
𝐴𝑐𝑐𝑢𝑟𝑎𝑡𝑖𝑜𝑛 𝑣𝑎𝑙𝑢𝑒 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑝𝑝𝑟𝑜𝑝𝑟𝑖𝑎𝑡𝑒 𝑟𝑒𝑐𝑜𝑚𝑚𝑒𝑛𝑑𝑎𝑡𝑖𝑜𝑛
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑟𝑒𝑐𝑜𝑚𝑚𝑒𝑛𝑑𝑎𝑡𝑖𝑜𝑛× 100% (1)
To obtain number of appropriate recommendation, users are given the option
of two buttons, a "maybe" button and a "no" button. The "maybe" button is selected if
the recommendation given is appropriate to the user, while the "no" button is selected
if the recommendation given is not appropriate to the user.
Table 2 Accuracy Testing
Object Number of Recommendation
Number of Appropriate Recommendation
Number of not Appropriate Recommendation
Accuracy Value
1 n n n n% 2 n n n n%
Average Accuracy n%
Result of accuracy testing will be inserted in accuracy testing table like shown
in Table 2.
The proposed method will be compared with combination of collaborative-filtering and content-based method. combination of collaborative-filtering and content-based method is commonly used to build an event recommendation system. the compared method will also be evaluated using accuracy testing.
4 Result and Discussion
In these section, it shows the experimental result of hybrid method (combination of collaborative filtering and sentiment analysis) implementation in developing event recommendation system. Accuracy testing is used to obtain experimental result, it calculate value between user accepted event recommendation and total event recommended by system. Total amount of event recommended by system is depend on user preferences that obtained from in app user interaction such as follow another user, comment on posted event, like posted event, and give rating to an posted event. before user get a recommended event, user must do the following interactions like above. recommended event total amount also affected by used method. The accuracy testing results can be seen in Table 3.
Table 3 Accuracy Testing Result of Collaborative Filtering and Sentyment Analysis Method
Object Number of Recommendation
Number of Appropriate Recommendation
Number of not Appropriate Recommendation
Accuracy Value
1 9 8 1 89% 2 9 7 2 78% 3 8 7 1 88% 4 10 8 2 80% 5 13 10 3 77% 6 13 12 1 92%
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Object Number of Recommendation
Number of Appropriate Recommendation
Number of not Appropriate Recommendation
Accuracy Value
7 18 16 2 89% 8 10 7 3 70% 9 13 12 1 92% 10 9 6 3 67% 11 12 11 1 92% 12 11 8 3 73% 13 11 10 1 91% 14 9 8 1 89% 15 7 4 3 57%
Average Accuracy 82%
Average accuracy value is obtained by calculate the average of all accuracy value. The
result is 82%.
In table 4 shown the average accuracy of commonly used method to build event
recommendation system.
Table 4 Accuracy Testing Result of Collaborative Filtering and Content-based Filtering
Method
Object Number of Recommendation
Number of Appropriate Recommendation
Number of not Appropriate Recommendation
Accuracy Value
1 17 12 5 70% 2 23 9 14 39% 3 24 7 17 29% 4 23 8 15 35% 5 18 8 10 44% 6 23 13 10 56% 7 26 19 7 73% 8 22 5 17 23% 9 23 11 12 48% 10 23 6 17 26% 11 24 10 14 42% 12 23 7 16 30% 13 25 10 15 40% 14 22 7 15 32% 15 17 3 14 17%
Average Accuracy 40%
5 Conclusion In this research, hybrid method is built from combination of collaborative filtering and sentiment analysis. user-based collaborative filtering is used to predict user rating based on user preferences and sentiment analysis is used to calculate sentiment score of user’s comments on an event. The final result of user rating prediction is the result of the adding user rating prediction with sentiment score. From the experiment result it shows that the average accuracy obtained from the proposed method (Combination of Collaborative filtering & Sentiment Analysis) is 82% while the average accuracy obtained from the comparison method (Combination of Collaborative filtering & Content Based) is 40%. This proves that the proposed method is better than the
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comparison method in the case of building a social media-based event recommendation system as was done in this study. The average accuracy value obtained from the comparison method is low because when compared to the proposed method, the comparison method has more recommendations. so that it affects the level of the resulting recommendations accuracy.
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