application of a hybrid method to build a mobile device

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

Dio Saputra Kudori et al. , Event Recommendation System: ... 109

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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




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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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




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.

References

1. Zhao, Z.D., Shang, M.S.: User-based Collaborative Filtering Recommendation Algorithms on Hadoop. Third International Conference on Knowledge Discovery and Data Mining (2010)

2. Adomavicius G., Tuzhilin A., Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowledge and Data Engineering, 17(6): 734-749 (2005)

3. Medhat, W., Hassan, A., Korashy, H.: Sentyment Analysis Algorithm and Applications: A

Survey. Ain Shams Engineering Journal, Volume 5, Issue 4, Pages 1093-1113 (2014) 4. Kayaalp, M., Ozyer, T., Ozyer, S., T.: A Collaborative and Content Based Event

Recommendation System Integrated With Data Collection Scrapers and Services at a Social Networking Site. International Conference on Advances in Social Network Analysis and Mining, Athens, Greece (2009)

5. Horowitz, D., Contreras, D., Salamo, M.: EventAware: A Mobile Recommender System for Events. Pattern Recognition Letters (2017)

6. Koren, Y., Bell, R.: Advances in collaborative filtering, in: Recommender Systems Handbook. Springer, pp. 145-186 (2011)

7. Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-aware recommender systems. AI Magazine 32, 67-80 (2011)

8. Rafsanjani, A., H., N., Salim, N., Aghdam, A., R., Fard, K., B.: Recommendation Systems: a

review. International Journal of Computational Engineering Research (2013) 9. Burke, R.: Hybrid Recommender Systems : Survey and Experiments. User Modeling and User

Adapted Interaction 12, 331-370 (2002).

10. Mo, Y., Li, B., Wang, B., Yang, L. T., Xu, M.: Event recommendation in social networks based on reverse random walk and participant scale control, Future Generation Computer Systems, Volume 79, Part 1 (2018).

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