scholarly journals Dynamic Modeling and Analysis of Multidimensional Hybrid Recommendation Algorithm in Tourism Itinerary Planning under the Background of Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yange Hao ◽  
Na Song
Keyword(s):  
Travel Time ◽  
User Satisfaction ◽  
Planning System ◽  
Personalized Recommendation ◽  
User Interest ◽  
Accuracy Rate ◽  
Recommendation Algorithm ◽  
Itinerary Planning ◽  
Travel Planning ◽  
Travel Recommendation

Smart tourism can provide high-quality and convenient services for different tourists, and tourism itinerary planning system can simplify tourists’ tourism preparation. In order to improve the limitation of the recommendation dimension of traditional travel planning system, this paper designs a mixed user interest model on the premise of traditional user interest modeling and combines various attributes of scenic spots to form personalized recommendation of scenic spots. Then, it uses heuristic travel planning cost-effective method to construct the corresponding travel planning system for travel planning. In terms of the accuracy rate of travel planning recommendation, the accuracy rate of multidimensional hybrid travel recommendation algorithm is 0.984, and the missing rate is 0. When the travel cost and travel time are the same and the number of scenic spots is 20–30, the memory occupation of MH algorithm is only 1/2 of that of TM algorithm. The results show that the multidimensional hybrid travel recommendation algorithm can improve the personalized travel planning of users and the travel time efficiency ratio. The results of this study have a certain reference value in improving user satisfaction with the travel planning system and reducing user interaction.

scholarly journals Key Technologies and Discrete Dynamic Modeling Analysis of Online Travel Planning System Based on Big Data Scenario Aware Service

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yange Hao ◽  
Na Song
Keyword(s):  
Recommendation System ◽  
Performance Test ◽  
Scene Perception ◽  
Search Space ◽  
Test Method ◽  
Planning System ◽  
Personalized Recommendation ◽  
Travel Planning ◽  
Key Technologies ◽  
Online Travel

The key technology of online travel recommendation system has been widely concerned by many Internet experts. This paper studies and designs a scenario aware service model in online travel planning system and proposes an online travel planning recommendation model which integrates collaborative filtering and clustering personalized recommendation algorithm. At the same time, the algorithm performance test method and model evaluation index are given. The results show that CTTCF algorithm can find more neighbor users than UCF algorithm, and the smaller the search space is, the more significant the advantage is. The number of neighbors is 5, 10, 15, 20, and 25, respectively, and the corresponding average absolute error values are about 0.815, 0.785, 0.765, 0.758, and 0.755, respectively. The scores of the six emotional travel itinerary recommendation schemes are all higher than 142 points. Only the two schemes have no obvious rendering effect. The proposed online travel itinerary planning scheme has potential value and important significance in the application of follow-up recommendation system. It solves the problem of low scene perception satisfaction in the key technologies of online tourism planning system.

scholarly journals Personalized Recommendation Algorithm for Web Pages Based on Associ ation Rule Mining

2018 ◽  
Vol 173 ◽  
pp. 03020
Author(s):  
Lu Xing-Hua ◽  
Ye Wen-Quan ◽  
Liu Ming-Yuan
Keyword(s):  
User Satisfaction ◽  
Personalized Recommendation ◽  
Web Pages ◽  
Semantic Features ◽  
Web Page ◽  
Web Crawler ◽  
Rule Mining ◽  
Recommendation Algorithm ◽  
Browsing Behavior ◽  
The Web

In order to improve the user ' s ability to access websites and web pages, according to the interest preference of the user, the personalized recommendation design is carried out, and the personalized recommendation model for web page visit is established to meet the personalized interest demand of the user to browse the web page. A webpage personalized recommendation algorithm based on association rule mining is proposed. Based on the semantic features of web pages, user browsing behavior is calculated by similarity computation, and web crawler algorithm is constructed to extract the semantic features of web pages. The autocorrelation matching method is used to match the features of web page and user browsing behavior, and the association rules feature quantity of user browsing website behavior is mined. According to the semantic relevance and semantic information of web users to search words, fuzzy registration is taken, Web personalized recommendation is obtained to meet the needs of the users browse the web. The simulation results show that the method is accurate and user satisfaction is higher.

scholarly journals Improved Personalized Recommendation Algorithm Based on Context-Aware in Mobile Computing Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Fei Long
Keyword(s):  
Mobile Computing ◽  
Personalized Recommendation ◽  
Mobile Service ◽  
Computing Environment ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Related Factors ◽  
Mobile Computing Environment ◽  
Collaborative Filtering Algorithm ◽  
User Interest Model

With the development and popularization of e-commerce and Internet, more and more attention has been paid to personalized recommendation for users. The traditional user interest model only considers the user’s behavior on the project, ignoring the user’s context at that time. Pointing to the shortage that context-related factors are not considered in previous works, combining the characteristics of a mobile computing environment, this paper studies the algorithm and model of mobile service recommendation. A recommendation algorithm based on specified context filtering in mobile computing environment is proposed. The context of the classification is aggregated, by grouping the scenarios of the same category together. Through experiments, we found that the improved personalized recommendation algorithms are superior to the common collaborative filtering algorithm.

Time-related factors influencing on an itinerary planning system

2016 ◽  
Vol 7 (1) ◽  
pp. 16-36 ◽  
Author(s):  
Jitimon Angskun ◽  
Sasiwimon Korbua ◽  
Thara Angskun
Keyword(s):  
User Satisfaction ◽  
Routing Algorithm ◽  
Planning System ◽  
Planning Systems ◽  
Related Factors ◽  
Content Type ◽  
Tourist Attractions ◽  
Itinerary Planning ◽  
Factors Influencing ◽  
Total Travel Time

Purpose – This paper aims to focus on time-related factors influencing on an itinerary planning system. The research objective is to produce an itinerary planning system which balances between the limited time of traveler and the number of tourist attractions they can visit. This system should facilitate travelers by presenting candidate itineraries that visit attractions as much as possible under several time-related factors. Design/methodology/approach – To achieve the goal, an itinerary planning system has been designed and developed. The system considers several time-related factors including acceptable total travel time specified by travelers, time-related factors at an attraction (e.g. time zones, opening hours and visiting time) and time-related factors of traveling (e.g. road obstructions, weather, date and time and rest time). A routing algorithm which is aware of these time-related factors has been introduced to find candidate itineraries. Findings – The performance of developed itinerary planning system has been evaluated by measuring speed and accuracy of seven traveling situations under different time-related factors. The experimental results indicate that the proposed routing algorithm spends less planning time than the traditional exhaustive routing algorithm. The efficiency of the proposed algorithm over the exhaustive algorithm is approximately 46 per cent while the accuracy is equal. Additionally, this designed system is evaluated by usability testing from nine experts. The evaluation is performed by measuring the user satisfaction level with the ability of user–system interaction. The results show that the overall system usability is in very satisfied level. Research limitations/implications – The designed itinerary planning system has three limitations. First, Google maps technology could not find information of some tourist attractions because these places were marked with several coordinates on the map. Second, holiday periods are manually kept into the database of system; therefore, it is necessary to annually and manually update the information. Third, the developed system is an online planner; thus, the speed of system depends on the bandwidth of users. Practical implications – The designed itinerary planning system considers time-related factors as much as possible and more than the existing planning systems. This implies that the designed system is one of the most accurate planning systems in practice. Thus, the tourism business could rely on the developed itinerary planning system to help travel agents plan a travel itinerary properly and receive an accurate and up-to-date travel explanation to their customers. Originality/value – This research proposes the novel design and implementation of an itinerary planning system which can suggest candidate itineraries, which visit maximum attractions under several time-related factors.

The Improved Collaborative Filtering Recommendation Algorithm Based on Cloud Model

2013 ◽  
Vol 411-414 ◽  
pp. 2292-2296
Author(s):  
Jia Si Gu ◽  
Zheng Liu
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Cloud Model ◽  
Personalized Recommendation ◽  
Test Results ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Model Based ◽  
Collaborative Filtering Algorithm

The traditional collaborative filtering algorithm has a better recommendation quality and efficiency, it has been the most widely used in personalized recommendation system. Based on the traditional collaborative filtering algorithm,this paper considers the user interest diversity and combination of cloud model theory.it presents an improved cloud model based on collaborative filtering recommendation algorithm.The test results show that, the algorithm has better recommendation results than other kinds of traditional recommendation algorithm.

Research on Web Application Technology in Distance Education Personalized Recommendation System

2013 ◽  
Vol 859 ◽  
pp. 416-421 ◽  
Author(s):  
Zhu Feng Qiao ◽  
Jian Xin Guo ◽  
Ji Chun Zhao
Keyword(s):  
Distance Education ◽  
Web Application ◽  
Recommendation System ◽  
Rapid Development ◽  
Personalized Recommendation ◽  
User Interest ◽  
Application Technology ◽  
Recommendation Algorithm ◽  
Teaching Resources ◽  
Collaborative Recommendation

With the rapid development of distance education, distance educations teaching resources has a number of large, but, it is difficult to find suitable courseware resources in time. The system constructs and improves the user interest model of the vector space, through the analysis of user behavior. The system uses the content-based recommendation algorithm, user-base collaborative recommendation and item-base collaborative recommendation algorithm to implement distance education resource recommender system. So as to provide distance education personalized recommendation web application technology as far as possible to meet the user needs, enhance the user experience of distance education system.

scholarly journals ATIPS: Automatic Travel Itinerary Planning System for Domestic Areas

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Hsien-Tsung Chang ◽  
Yi-Ming Chang ◽  
Meng-Tze Tsai
Keyword(s):  
User Preference ◽  
Planning System ◽  
Preference Learning ◽  
Departure Point ◽  
Trip Planning ◽  
Leisure Travel ◽  
Learning Mechanism ◽  
Itinerary Planning ◽  
Travel Planning ◽  
User Intentions

Leisure travel has become a topic of great interest to Taiwanese residents in recent years. Most residents expect to be able to relax on a vacation during the holidays; however, the complicated procedure of travel itinerary planning is often discouraging and leads them to abandon the idea of traveling. In this paper, we design an automatic travel itinerary planning system for the domestic area (ATIPS) using an algorithm to automatically plan a domestic travel itinerary based on user intentions that allows users to minimize the process of trip planning. Simply by entering the travel time, the departure point, and the destination location, the system can automatically generate a travel itinerary. According to the results of the experiments, 70% of users were satisfied with the result of our system, and 82% of users were satisfied with the automatic user preference learning mechanism of ATIPS. Our algorithm also provides a framework for substituting modules or weights and offers a new method for travel planning.

scholarly journals A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hailong Chen ◽  
Haijiao Sun ◽  
Miao Cheng ◽  
Wuyue Yan
Keyword(s):  
Collaborative Filtering ◽  
Personalized Recommendation ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Weighting Method ◽  
Trust Value ◽  
Recommendation Algorithms ◽  
Similarity Calculation ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Accuracy

Collaborative filtering recommendation algorithm is one of the most researched and widely used recommendation algorithms in personalized recommendation systems. Aiming at the problem of data sparsity existing in the traditional collaborative filtering recommendation algorithm, which leads to inaccurate recommendation accuracy and low recommendation efficiency, an improved collaborative filtering algorithm is proposed in this paper. The algorithm is improved in the following three aspects: firstly, considering that the traditional scoring similarity calculation excessively relies on the common scoring items, the Bhattacharyya similarity calculation is introduced into the traditional calculation formula; secondly, the trust weight is added to accurately calculate the direct trust value and the trust transfer mechanism is introduced to calculate the indirect trust value between users; finally, the user similarity and user trust are integrated, and the prediction result is generated by the trust weighting method. Experiments show that the proposed algorithm can effectively improve the prediction accuracy of recommendations.

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