AN AGENT-BASED MOBILE RECOMMENDER SYSTEM FOR TOURISMS

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1 AN AGENT-BASED MOBILE RECOMMENDER SYSTEM FOR TOURISMS 1 FARNAZ DERAKHSHAN, 2 MAHMOUD PARANDEH, 3 AMIR MORADNEJAD 1,2,3 Faculty of Electrical and Computer Engineering University of Tabriz, Tabriz, Iran 1 derakhshan@tabrizu.ac.ir, 2 m.parandeh92@ms.tabrizu.ac.ir, 3 amir.moradnejad92@ms.tabrizu.ac.ir Abstract- Nowadays using recommender systems in tourism applications has been increased. Recommender systems help users for reduction of their decision time and generate recommendations based on their preferences. In this paper, we proposed a new method for updating users profile and introduced a general architecture for agent-based mobile recommender system. Agent-based design permits to model different kinds of activities in a flexible way. In this system, content-based filtering algorithm for recommender system is used. The prototype of our mobile application has implemented for Tabriz tourism. Index terms- Multi-Agent, Recommendation System, Mobile, Tourism, Content Filtering. I. INTRODUCTION As information expands, it becomes possible to search and select information from tons of available options. Users may not have enough time or even knowledge to access this information. Recommender System (RS) is a possible solution to resolve this problem which uses information filtering techniques to assist users and gives them personalized recommendation. It offers recommendations of various items to users based on their preferences [7]. Recommender systems are usually used in websites which have millions of visitors where users can receive recommendations for movies, books, music and videos [8]. But today, recommender systems are getting popular in the tourism domain where personalized services are recommended to travelers. Tourism is an activity mainly related to personal interests and preferences of people. Many online websites provide information and assistance for planning holidays. However, these systems are not considered so successful applications for purchasing items or suggesting movies. Because, tourism is not a frequent activity and there is not enough available rating in comparison to other domains. Therefore, poor recommendations may waste travelers' time. Recent development in etourism lies strongly in the use of mobile devices as a primary platform. Also, mobile devices are available anywhere and anytime. Mobile phones (especially smart phones) that are equipped with GPS, enable more personalized recommendation based on location of user. There are many recommender systems that proposed for tourism domain. Most of these applications are based on Multi-agent Systems (MASs). Multi-agent is a system that consists of a set of agents which interact with each other to reach their owners desired objectives. Agents usually cooperate, coordinate and negotiate with each other to solve complex problems. Agents and multi-agent systems allow modeling, at a very high level, heterogeneous and distributed systems and environments [9]. In tourism applications, multi-agent systems can help for following reasons: Agent technology specially used in distributed systems. Each user in system, has special user agent to handle user request. In recommender system, a recommender agent that keeps users profile and generates recommendation based on this profile should be defined. One activity agent is used to add, delete and update attractions in tourism system. In this paper, we also propose a multi-agent mobilebased recommender system that uses content based filtering [4] technique to generate recommendation. The first improvement of our work is that we overcome cold start problem (cold start problem is that when a user registers to system, there is not any information of user to use for recommendation) by asking a few questions when a new user registered. The second advantage of our implementation is that we have applied this technique in an application for Tabriz Tourism for the first time. Having this introduction, the remaining of this paper is organized as follows: Section 2 introduces related works and the existing tourism applications based on multi-agent. In Section 3, we propose our model. It is followed by analyses and simulation of our model in Section 4. Finally, in Section 5 we conclude the paper. II. REVIEW OF RELATED WORKS In this section, we overview some of the related works which use multi-agent based recommendation systems for tourism applications. In [1] in 2014, the authors proposed a multi agent based recommender system for tourism application. 30

2 In [1] in 2014, the authors proposed a multi agent based recommender system for tourism application. The proposed system uses various types of agents (user agent, recommendation agent and activity agent) which collaborate with each other to gain information from data repositories, then they send information to recommender system. After that a list of recommendation (which consists of hotels, places and restaurants based on users query) will be presented for users. Recommender system uses reputation based collaborative filtering (RbCF) algorithm [2] [10]. Reputation vector is calculated by user stimulatory metrics (restaurant reputation, hotel reputation and place reputation). The main advantage of this approach is that it overcomes cold start problem of collaborating filtering based algorithms. Cold start problem means users do not have any interaction with system when they newly registered; therefore, recommendation is hard for them. Authors in [3] in 2012 proposed a multi agent system based application called Turist@. Turist@ consists of a set of agents such as user agent, broker agent, recommender agent and activity agent. The main difference of this approach is considering attractions that the user can be aware in the destination he/she has arrived. Therefore, dynamic recommendation is used to achieve this goal. Users' preferences are evaluated by explicit and implicit methods. Recommender system uses hybrid approaches (combination of content based and collaborative filtering). When the number of users and their interactions with system are low, contentbased filtering has better result. On the other hand, while the number of users grows, system uses collaborative filtering to generate recommendation. The cold start problem is overcome by this hybrid approach. Recommender system is important for tourism applications. Based on survey on [5], collaborative and content-based filtering are used more than others in tourism recommender system. Because of the growth of using smartphone mobile phones, recommender systems are very popular nowadays. Mobile devices that are equipped with GPS, enable location-aware recommender system. Moreover, because of the nature of mobile phones, information can be accessed anytime and anywhere. So, recommendation can be accurate and personalized. The services offered by mobile recommender systems in tourism are different. Recommendation of attractions is more beneficiary in mobile devices because mobile uses more contextual parameters like location, time, weather, transport mode, user mode and social environment. III. PROPOSED METHOD After studying previous works, we propose a general model for multi-agent recommender systems in tourism. In our model we consider three major agents (user agent, recommender agent and activity agent) described as follows: User agent is run on client and responsible for preparing GUI for users, gets input from users and presents appropriate result to users. Moreover, user agent communicates with recommender agent and gets result from it. Recommender agent uses users preferences to generate recommendation in server side. This agent communicates with activity agent to store and update information about places and attractions. Activity agent is used for storing and removing information of tourist attractions including places, restaurants and museums in server by using sub agents. Each sub agents can do specific job (adding or removing information of restaurant). Activity agent prepares information for recommender agent. Figure 1 shows the architecture of our model. Figure 1. Architecture of Multi agent Recommender system In this model, we use content based recommendation algorithm for generating recommendation. In this approach, when users signed up to system, they should answer a few questions about their preferences. After that, the users' profile vector is created and sent to recommender agent. Recommender system uses this vector to recommend attractions. The weighted Euclidean distance approach is used for making matches between user profile and the set of attractions [3] [6]. Its formula shows as follow: Where, 31

3 The user profile is updated when the user rates specific attraction. The user has five options to give a score to specific attraction (scores are very bad, bad, good, very good, and excellent). In table 1, we illustrate each score related to each option. TABEL 1. having a list of all attractions, hotels and restaurants of Tabriz. In next section, we'll introduce our mobile application more precisely. A. Application Specification In Tabriz Tourism Application we have used windows-based mobile phone as the user client. C# is the language used to implement our application on windows-based phone. In server side, we have used PHP code and MySQL as database engine to handle recommendation processes. The main specifications and requirements of our application can be described as below: a) The app. is multi-agent based and mobile based. b) The language used in client side is C# and SQLite. c) The language used in server side is PHP and MySQL. d) It is user friendly. e) Internet is required for recommendation process. f) GPS is required for navigation. For better understanding, assume that a user profile is u = {0.2, 0.4, 0.4, 0.6} and the user rates very good to attraction with a = {0.1, 0.3, 0.5, 0.4} and In this approach, every rated attraction by the user will be directly affected to his/her profile vector. IV. BASIC STRUCTURE OF REGULAR 16- BIT CSLA We implemented our proposed model for Tabriz for the first time. In Tabriz, there is not any mobile application which uses user's preferences to suggest attractions to users. Primarily, in order to implement recommendation system, we created a data base Simple queries (such as getting general information about specific attraction or the list of attractions) have been implemented in client side, so the response time would be very quick and user agent can handle this kind of tasks. However, rating attractions and generating recommendation are needed to communicate with server. Our mobile application is bilingual. Users can choose ether Persian or English language. Also, users are able to search whole attractions and select each one. There are two other options that users can use for searching places. First, recommendation section, in this section the best attraction which is more matched to user s profile is recommended to user. Second, closer places, user can see all the attractions that is in specific radius from itself. B. Application s Demo In this Section we describe how this application works by using examples presented in Figure 2 to 6. After installing the application on a user s mobile phone, the user has to register in Registration phase, after answering some personal questions to create ID and password, some basic questions about user's preferences are asked (Figure 2). When the user answers these questions, his/her profile vector is created. After answering questions, the user can see all of Tabriz attractions that are available in system, then he/she can get information or search (Figure 3). If the user swipes left, the recommendation section appears (Figure 4). When the user taps recommender button, recommender agent gets user's ID and then searches database for user profiles, after it retrieves user profile, compares it with existing attraction with the help of data that is stored with activity agent. The 32

4 attraction center that is close to the user profile is selected as a recommendation and is sent back to the user agent. User agent prepares attraction and shows it to the user. The user, then, can select the recommended attraction center and see the information about it and also rate it. Figure 6. Navigation section Figure 2. Registration phase Figure 3. Attractions list Figure 4. Recommendation Sec. Figure 5. Attraction Details In Figure 5, a short description of the attraction place chosen by user is presented. In this section, the user can read history of attraction and see total rate of that place (presented by yellow stars). Also, user can navigate to selected attraction (Figure 6) and rate it after visiting the place. In navigating section, user s current location and destination are used to find the best route. C. Advantage of our application Our app is the first recommendation mobile app for Tabriz. Earlier tourist applications for Tabriz were usually web based (such as or but recently one mobile app called Mosafer Tabriz is created. This app shows attractions and navigates users to specific destination. Mosafer Tabriz does not use any personalization to recommend attraction to users. The disadvantages of using web application are that users must access internet to see list of attractions, in addition, most of these web applications are not user friendly. In comparison with the web-based applications, our application has the following advantages: Using mobile devices. So users can access it everywhere. Considering user preferences. Our app recommends specific attraction for each user based on his/her profile. Ease of use. It has user friendly interface. Offline. You can still use it without internet. Recommender system is the part which needs internet. Most of tourism apps run in websites and it is hard to use them when you do not have internet connection. In addition, most of our websites do not use GPS device to navigate tourist to specific attraction. Therefore, our mobile application overcomes these problems with GPS enabled and mobility of smartphones. It is necessary to mention that, as there are some limitations to use some of international social networks in our country, we could not consider using social networks in our system. Thus, we had limitations for easily accessing to users other profiles and analyze them for providing recommendation. CONCLUSION In this paper, we proposed the first bilingual (Persian and English) mobile recommendation application for 33

5 Tabriz. This application implements with multi-agent systems and uses content based recommendation algorithm for generating recommendation. User's profile vector is created when the user registers to system and updated every time when the user rates to specific attraction. We overcame cold start problem of recommender system with asking few questions rom user. The result of our work is helping tourists to have a better traveling experience when they visit Tabriz. Further studies can be suggested and carried out using hybrid recommendation algorithms which have improved recommendation process and also improved apps ability such as proactive suggestion when user is near to specific attraction that satisfy user preference. REFERENCES [1] P. Bedi, S. K. Agarwal, V. Jindal, Richa, MARST: Multi- Agent Recommender System for e-tourism Using Reputation Based Collaborative Filtering, Springer International Publishing Switzerland, pp , [2] X. Su, T. M. Khoshgoftaar, A Survey of Collaborative Filtering Techniques, Advances in Artificial Intelligence, 19 pages, [3] M. Batet, A. Moreno, D. Sánchez, D. Isern, A. Valls, Turist@: Agent-based personalised recommendation of tourist activities, Expert Systems with Applications, pp , [4] R. V. Meteren, M. V. Someren, Using Content-Based Filtering for Recommendation, ECML/MLNET Workshop on Machine Learning and the New Information Age, pp , [5] D. Gavalas, Ch. Konstantopoulos, K. Mastakas, G. Pantziou, Mobile recommender systems in tourism, Journal of Network and Computer Applications, pp , 2014 [6] S. Debnath, N. Ganguly and P. Mitra, Feature weighting in content based recommendation system using social network analysis, In 17th international conference on World Wide Web, pp , [7] F. Ricci, L. Rokach, B. Shapira, P.B. Kantor, Recommender Systems Handbook, Springer, New York 2011 [8] D. Jannach, M. Zanker, A. Felfernig, G. Friedrich, Recommender Systems - An Introduction, Cambridge University Press, New York 2011 [9] M. Wooldridge, An introduction to multiagent systems, John Wiley, 2009 [10] J. S. Breese, D. Heckerman, C. Kadie Empirical analysis of predictive algorithms for collaborative filtering, UAI 98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pp ,

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