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 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 User Preference Learning-Based Edge Caching for Fog Radio Access Network

2019 ◽  
Vol 67 (2) ◽  
pp. 1268-1283 ◽  
Author(s):  
Yanxiang Jiang ◽  
Miaoli Ma ◽  
Mehdi Bennis ◽  
Fu-Chun Zheng ◽  
Xiaohu You
Keyword(s):  
Access Network ◽  
User Preference ◽  
Preference Learning ◽  
Radio Access Network ◽  
Radio Access ◽  
Fog Radio Access Network ◽  
Edge Caching

User Preference Learning for Online Social Recommendation

2016 ◽  
Vol 28 (9) ◽  
pp. 2522-2534 ◽  
Author(s):  
Zhou Zhao ◽  
Hanqing Lu ◽  
Deng Cai ◽  
Xiaofei He ◽  
Yueting Zhuang
Keyword(s):  
User Preference ◽  
Preference Learning ◽  
Social Recommendation

Aspect-Oriented Recommender Systems

2013 ◽  
pp. 55-72
Author(s):  
Punam Bedi ◽  
Sumit Kr Agarwal
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Recommender System ◽  
User Preference ◽  
Preference Learning ◽  
Decision Making Process ◽  
Design Issues ◽  
Aspect Oriented Programming ◽  
Multi Agent ◽  
Oriented Approach

Recommender systems are widely used intelligent applications which assist users in a decision-making process to choose one item amongst a potentially overwhelming set of alternative products or services. Recommender systems use the opinions of members of a community to help individuals in that community by identifying information most likely to be interesting to them or relevant to their needs. Recommender systems have various core design crosscutting issues such as: user preference learning, security, mobility, visualization, interaction etc that are required to be handled properly in order to implement an efficient, good quality and maintainable recommender system. Implementation of these crosscutting design issues of the recommender systems using conventional agent-oriented approach creates the problem of code scattering and code tangling. An Aspect-Oriented Recommender System is a multi agent system that handles core design issues of the recommender system in a better modular way by using the concepts of aspect oriented programming, which in turn improves the system reusability, maintainability, and removes the scattering and tangling problems from the recommender system.

MULTIPLE: Multi-level User Preference Learning for List Recommendation

2021 ◽  
pp. 221-236
Author(s):  
Beibei Li ◽  
Beihong Jin ◽  
Xinzhou Dong ◽  
Wei Zhuo
Keyword(s):  
User Preference ◽  
Preference Learning ◽  
Multi Level
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