Intelligent Tourist Recommender System Focused on Collective Profiles

Group recommendation is complex due to the selection procedure, structure and group conduction could conditioning negatively its effectiveness. Aspects like expectations of its components, the group size, time, communication standards, the previous experience or condition of members could have a negative influence. World Tourism Organization (UNWTO) defines tourism as a social, cultural and economic phenomenon which entails the movement of people to countries or places outside their usual environment for personal or business purposes. These people are called visitors (which may be either tourist or excursionists; resident or non-residents) and tourism has to do with their activities, some of which involve tourism expenditure. International tourism now represents 7% of the world’s exports of goods and services, up from 6% in 2014, as tourism has grown faster than world trade over the past four years. Holidays, recreation and other forms of leisure have been just over half of all international tourist arrivals in 2015 (53% or 632 million). Business and professional purposes accounted for some 14% of all international tourists, another 27% travelled for other reasons such as visiting friends and relatives (VFR), religious reasons and pilgrimages, health treatment. The purpose of visit for the remaining 6% of arrivals was not specified. Nowadays, the greater part of tourists around the world plan their vacation, make reservations or buy services, moreover, they share their experiences through the Internet. In this research is implemented an intelligent system for managing and recommending tourist places to collective profiles, which is able to identify and satisfy preferences of group members

An intelligent hybrid multi-criteria hotel recommender system using explicit and implicit feedbacks

Recommender systems, also known as recommender engines, have become an important research area and are now being applied in various fields. In addition, the techniques behind the recommender systems have been improved over the time. In general, such systems help users to find their required products or services (e.g. books, music) through analyzing and aggregating other users’ activities and behavior, mainly in form of reviews, and making the best recommendations. The recommendations can facilitate user’s decision making process. Despite wide literature on the topic, using multiple data sources of different types as the input has not been widely studied. Recommender systems can benefit from the high availability of digital data to collect the input data of different types which implicitly or explicitly help the system to improve its accuracy. Moreover, most of the existing research in this area is based on single rating measures in which a single rating is used to link users to items. This dissertation aims to design a highly accurate hotel recommender system, implemented in various layers and tailored for the subject problem. Using multi-rating system and benefitting from large-scale data of different types, the recommender system suggests hotels that are personalized and tailored for the given user. The system employs natural language processing techniques to assess the sentiment of the users’ reviews and extract implicit features. The entire recommender engine contains multiple sub-systems, namely users clustering, matrix factorization module, and hybrid recommender system. Each sub-system contributes to the final composite set of recommendations through covering a specific aspect of the problem. The accuracy of the proposed recommender system has been tested intensively where the results confirm the high performance of the system