scholarly journals On Ontology-Based Tourist Knowledge Representation and Recommendation

2019 ◽  
Vol 9 (23) ◽  
pp. 5097
Author(s):  
Pai ◽  
Wang ◽  
Hsu ◽  
Lin ◽  
Chen
Keyword(s):  
Knowledge Representation ◽  
Virtual Communities ◽  
Information Overload ◽  
Rapid Development ◽  
Current Location ◽  
Technology Age ◽  
Travel Articles ◽  
Online Travel Reviews ◽  
Travel Decision ◽  
Online Travel

In the rapid development of the information technology age, many travelers search for travel articles through the Internet. These travel articles include the experience and knowledge of traveler, which can be used as a reference for tourism planning and attraction selection. At present, the most travel experience and knowledge is available in online travel reviews (OTR). OTR and eWOM (electronic word-of-mouth) contain a lot of knowledge of consumers and travelers. Many travelers often look for OTR content through virtual communities, blogs, and search engine, but the search results often cause information overload problems. In addition, through virtual communities, blogs, and search engines, an OTR search still requires using keywords. However, most travelers cannot know the name of the attraction; therefore, travelers cannot use the correct keywords to search. That causes travelers to be unable to get enough information from OTR and unable to make the best travel plan. Therefore, this study focuses on the ontology-based tourist knowledge representation and recommendation method. And the study is to search for popular attractions from the OTR content and construct a tourist knowledge structure for these travelers. When the tourists do not need to know the keywords of the popular attraction name, they just need to get their current location; and then ORT content will recommend the next attraction to the traveler, which helps the traveler make the correct travel decision. The evaluation result showed that the method proposed in this study can help the travelers to quickly make the travel decision and is better than the traditional searching methods.

scholarly journals Scuba Diving Experience and Sustainability: An Assessment of Online Travel Reviews

2018 ◽  
Vol 9 ◽  
pp. 43-52 ◽  
Author(s):  
Peunjodi Naidoo ◽  
Prabha Ramseook-Munhurrun ◽  
Jing Li
Keyword(s):  
Text Analysis ◽  
Physical Environment ◽  
Scuba Diving ◽  
Tourism Industry ◽  
Small Island ◽  
Analysis Software ◽  
Unsupervised Analysis ◽  
Online Travel Reviews ◽  
Tourism And Hospitality ◽  
Online Travel

Scuba diving is a popular activity in small island destinations which is on the rise. However, it is particularly important to preserve the physical environment for small island developing states due to their unique biodiversity and fragile ecosystems. Scuba diving tourism in island destinations is provided mainly by dive operators who are responsible to deliver the scuba diving experience to tourists. However, despite the importance of sustainability for the tourism industry, it is unclear to which extent the marine environment or green issues are important for consumers. Studies are increasingly suggesting that sustainability is an important feature considered by consumers. However, information is sparse regarding the extent to which sustainability is a key component for customers when evaluating the scuba diving experience. In this study, 3109 text reviews from the Trip Advisor website across all 57 listed diving operators in Mauritius were selected for data analysis. Th e present study uses Leximancer, a text analysis software that conducts unsupervised analysis of natural language texts provided in an electronic format.The Gaze: Journal of Tourism and Hospitality Vol.9 2018 p.43-52

scholarly journals Personalized Recommendation Model of High-Quality Education Resources for College Students Based on Data Mining

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chaohua Fang ◽  
Qiuyun Lu
Keyword(s):  
Learning Process ◽  
Data Science ◽  
Recommendation System ◽  
Information Overload ◽  
Rapid Development ◽  
Learning Ability ◽  
Personalized Recommendation ◽  
Learning Resources ◽  
Learner Model ◽  
Internet Education

With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered.

Privacy and Security for Virtual Communities and Social Networks

2013 ◽  
pp. 19-31 ◽  
Author(s):  
Georgios Michaelides ◽  
Gábor Hosszú
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Virtual Communities ◽  
Personal Information ◽  
Rapid Development ◽  
Virtual Community ◽  
Privacy And Security ◽  
Internet Privacy ◽  
Large Audience ◽  
Privacy Risks

The importance of the virtual communities’ privacy and security problems comes into prominence by the rapid development of online social networks. This article presents the multiple threats currently plaguing the virtual world, Internet privacy risks, and recommendations and countermeasures to avoid such problems. New generations of users feel comfortable publishing their personal information and narrating their lives. They are often unaware how vulnerable the data in their public profiles are, which a large audience daily accesses. A so-called digital friendship is built among them. Such commercial and social pressures have led to a number of privacy and security risks for social network members. The article presents the most important vulnerabilities and suggests protection methods and solutions that can be utilized according to the threat. Lastly, the authors introduce the concept of a privacy-friendly virtual community site, named CWIW, where privacy methods have been implemented for better user protection.

A User Profile Awareness Service Collaborative Recommendation Algorithm Under LBSN Environment

2019 ◽  
Vol 28 (03) ◽  
pp. 1950008
Author(s):  
Mingjun Xin ◽  
Lijun Wu ◽  
Shunxian Li
Keyword(s):  
Social Influence ◽  
Rapid Development ◽  
User Profile ◽  
Mobile Internet ◽  
Superior Performance ◽  
Mobile Service ◽  
Data Sparsity ◽  
Collaborative Recommendation ◽  
Current Location ◽  
Sparsity Problem

Nowadays, location-based social network (LBSN) has become one of the most popular applications with the rapid development of mobile Internet. However, due to the spatial and real-time properties, mobile service recommendation under LBSN environment faces too many challenges especially data sparsity problem. To tackle these challenges, a recommendation framework is proposed in this paper which has four layers defined as data collection layer, user profile modeling layer, information processing layer and recommendation feedback layer, respectively. Furthermore, the ISC-CF algorithm is implemented to integrate users’ interest profile, social influence and current location context to effectively overcome the data sparsity problem. Thus, the social influence is quantified by a modified measure way. Finally, a dynamic and personalized adjustment algorithm is built by using the users’ profile tracking and the current location context. The experiment results show that the algorithm proposed in this paper has significantly superior performance compared with the other baseline recommendation methods in both hometown area and out-of-town area.

The Next Generation of Personalization Techniques

2011 ◽  
pp. 72-92
Author(s):  
Gulden Uchyigit
Keyword(s):  
Knowledge Representation ◽  
Traditional Method ◽  
Domain Knowledge ◽  
Information Overload ◽  
State Of The Art ◽  
User Preferences ◽  
Future Trends ◽  
User Profiles ◽  
User Interests ◽  
Key Concepts

Coping with today’s unprecedented information overload problem necessitates the deployment of personalization services. Typical personalization approaches model user preferences and store them in user profiles, used to deliver personalized content. A traditional method for profile representation is the so called keyword-based representation, where the user interests are modelled using keywords which are selected from the contents of the items which the user has rated. Although, keyword based approaches are simple and are extensively used for profile representation they fail to represent semantic-based information, this information is lost during the pre-processing phase. Future trends in personalization systems necessitate more innovative personalization techniques that are able to capture rich semanticbased information during the representation, modelling and learning phases. In recent years ontologies (key concepts and along with their interrelationships) to express semantic-based information have been very popular in domain knowledge representation. The primary goal of this chapter is to present an overview of the state-of-the art techniques and methodologies which aim to integrate personalization technologies with semantic-based information.

An Improved Real-Time Recommendation for Microblogs Based on Topic

2019 ◽  
Vol 34 (02) ◽  
pp. 2059006
Author(s):  
Yikui Shi ◽  
Jiyan Liu ◽  
Lei Shi ◽  
Jianwen Zhao ◽  
Na Su
Keyword(s):  
Real Time ◽  
Information Overload ◽  
Rapid Development ◽  
Search Space ◽  
Decay Function ◽  
Recommendation Algorithm ◽  
User Similarity ◽  
User Clustering ◽  
Intermediate Variables ◽  
Over Time

With the rapid development of the Internet, people are confronted with information overload. Many recommendation methods are designed to solve this problem. The main contributions of recommendation methods proposed in this paper are as follows: (1) An improved collaborative filtering recommendation algorithm based on user clustering is proposed. Clustering is performed according to user similarity based on the user-item rating matrix. So the search space of recommendation algorithm is reduced. (2) Considering the factor that user’s interests may dynamically change over time, a time decay function is introduced. (3) A method of real-time recommendation based on topic for microblogs is designed to realize real-time recommendation effectively by preserving intermediate variables of user similarity. Experiments show that the proposed algorithms have been improved in terms of running time and accuracy.

E-Commerce Personalized Recommendation

2014 ◽  
Vol 989-994 ◽  
pp. 4996-4999 ◽  
Author(s):  
Yan Zhang
Keyword(s):  
Information Retrieval ◽  
Electronic Commerce ◽  
Association Rule ◽  
Information Overload ◽  
Rapid Development ◽  
Personalized Recommendation ◽  
Application Layer ◽  
Advantages And Disadvantages ◽  
Theory Research ◽  
Content Based Filtering

With the rapid development of electronic commerce, the problem of "information overload" leads to the difficulty that user can't search the required goods effectively , personalized recommendation technology has been applied in e-commerce and popularization. By using the method of qualitative analysis of the current e-commerce site, the paper compare the information retrieval, association rule, content-based filtering and collaborative filtering four main recommendation technologies and analysis the advantages and disadvantages in the application layer, the recommendation technologies are introduced to review e-commerce research hot topic in the field of personalized recommendation, and analysis the current domestic e-commerce personalized recommendation theory research and application status, finally propose the challenges faced by e-commerce personalized recommendation domain.

scholarly journals A Probability-Based Hybrid User Model for Recommendation System

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jia Hao ◽  
Yan Yan ◽  
Guoxin Wang ◽  
Lin Gong ◽  
Bo Zhao
Keyword(s):  
Product Design ◽  
Recommendation System ◽  
Information Overload ◽  
Rapid Development ◽  
User Model ◽  
Information Communication ◽  
Design Cycle ◽  
Available Information ◽  
User Ratings ◽  
Content Based Filtering

With the rapid development of information communication technology, the available information or knowledge is exponentially increased, and this causes the well-known information overload phenomenon. This problem is more serious in product design corporations because over half of the valuable design time is consumed in knowledge acquisition, which highly extends the design cycle and weakens the competitiveness. Therefore, the recommender systems become very important in the domain of product domain. This research presents a probability-based hybrid user model, which is a combination of collaborative filtering and content-based filtering. This hybrid model utilizes user ratings and item topics or classes, which are available in the domain of product design, to predict the knowledge requirement. The comprehensive analysis of the experimental results shows that the proposed method gains better performance in most of the parameter settings. This work contributes a probability-based method to the community for implement recommender system when only user ratings and item topics are available.

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