HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario

Abstract

Nowadays, Physical Web together with the increase in the use of mobile devices, Global Positioning System (GPS), and Social Networking Sites (SNS) have caused users to share enriched information on the Web such as their tourist experiences. Therefore, an area that has been significantly improved by using the contextual information provided by these technologies is tourism. In this way, the main goals of this work are to propose and develop an algorithm focused on the recommendation of Smart Point of Interaction (Smart POI) for a specific user according to his/her preferences and the Smart POIs’ context. Hence, a novel Hybrid Recommendation Algorithm (HyRA) is presented by incorporating an aggregation operator into the user-based Collaborative Filtering (CF) algorithm as well as including the Smart POIs’ categories and geographical information. For the experimental phase, two real-world datasets have been collected and preprocessed. In addition, one Smart POIs’ categories dataset was built. As a result, a dataset composed of 16 Smart POIs, another constituted by the explicit preferences of 200 respondents, and the last dataset integrated by 13 Smart POIs’ categories are provided. The experimental results show that the recommendations suggested by HyRA are promising.

Analysis of the Influence of Hybrid Personalized Recommendation Algorithm on the Employment Tendency of Hotel Management Major

“…Literature [25] proposed a new user recommendation algorithm based on Mahout to solve the problems of difficulty and inefficiency in recommending new users without historical scores or behavior data in the background of big data and implemented distributed computing in the framework of Map Reduce to improve the ability of the algorithm to handle data scale. Literature [26] puts forward the direction that the recommendation system should break through in the current era of big data and makes use of the advantages of big data processing platform to process and utilize a large number of user feedback data and social network information, so as to further improve the quality of the recommendation list of the recommendation system and the satisfaction degree of users to the recommendation system. Literature [27] proposes an improved collaborative filtering recommendation algorithm, which combines factors such as the category of items and the user's interest degree, calculates the category distance between items by constructing a matrix of item categories, and measures the user's interest degree in a novel way.…”

On-site Trip Planning Support System Based on Dynamic Information on Tourism Spots

“…Furthermore, another category of existing studies proposes route recommendation methods that include multiple tourism spots [24][25][26] (category 7). These methods have the advantage of being less troublesome for users, but have the disadvantage of providing little diversity of tourism patterns.…”

Transforming Communication Channels to the Co-Creation and Diffusion of Intangible Heritage in Smart Tourism Destination: Creation and Testing in Ceutí (Spain)

“…The target respondent of this survey was members of the population over 18 years old that resided in the Region of Murcia in 2016, as the last data obtained by the census (Table 1). The survey was designed to research the user's habits during their tourism experience and to support the development of a recommendation algorithm called "HyRa: Hybrid Recommendation Algorithm focused on Smart POIs" [59], which could be adapted in the final solution. The survey was structured as three sections:…”

“…The next steps of Be Memories are promising. As a technological tool, the HyRA algorithm [59,60] will be included in the tool to personalize new routes for the users. In addition, Open Data about the city and environment will improve the recommendations through artificial intelligence.…”