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 Design of Personalized Recommendation System for Swimming Teaching Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhan Shi ◽  
Wei Wang
Keyword(s):  
Deep Learning ◽  
Recommendation System ◽  
The Body ◽  
Test Method ◽  
Personalized Recommendation ◽  
Survey Method ◽  
Sporting Event ◽  
Motivation For Learning ◽  
Great Confidence ◽  
The Right

Swimming is not only an entertaining hobby but also a sporting event. It is a sport for strengthening the body. Although there are many swimming coaches, there are different swimming teaching courses. However, choosing the right swimming instructor or course is the motivation for learning swimming activities. To this end, this paper conducts related research on the personalized recommendation system for swimming teaching based on deep learning with the purpose of improving the accuracy of the recommendation system to meet the needs of the users and promote the development of swimming events. This article mainly uses the experimental test method, the system construction method, and the questionnaire survey method to analyze and study the personalized swimming teaching system and the students’ attitude to it and draw a conclusion finally. The data results show that the accuracy of the system designed in this paper can meet the basic requirements. Hence, it can bring an excellent experience to the users. According to the questionnaire data, 85%–95% of people have great confidence in the personalized recommendation system.

scholarly journals A Movie Recommendation using Common Genre Relation on User-Item Subgroup

2019 ◽  
Vol 9 (1S3) ◽  
pp. 193-199
Keyword(s):  
Recommendation System ◽  
Side Information ◽  
Individual Preference ◽  
Graph Model ◽  
Search Space ◽  
Vital Role ◽  
Personalized Recommendation ◽  
Movie Recommendation ◽  
The Individual ◽  
The Empirical Analysis

Movie recommendation system has played a vital role in retrieving the movies that are of interest to the user. Most of the traditional methods provide a unified recommendation without considering the individual preference of the user. To address this challenge, various recommender methods are currently employing side information like location, time, gender, and genre to provide a personalized recommendation. In this paper, we propose —Common Genre Relations (COGS), which incorporates the information on genre relationships between the movies. Meanwhile, the method reduces the search space for each user and helps to mitigate the sparsity problem. To improve the scalability, the methods are executed on user-item subgroups. Extensive experiments are conducted on a real-world dataset. The empirical analysis shows that the proposed method based on the graph model excels the accuracy at top-k than the state-of-art collaborative filtering methods.

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.

Legal document recommendation system: A cluster based pairwise similarity computation

2021 ◽  
pp. 1-13
Author(s):  
Jenish Dhanani ◽  
Rupa Mehta ◽  
Dipti Rana
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Real Life ◽  
Citation Network ◽  
Search Space ◽  
Pairwise Similarity ◽  
Large Numbers ◽  
Legal Document ◽  
Legal Domain ◽  
Similarity Scores

Legal practitioners analyze relevant previous judgments to prepare favorable and advantageous arguments for an ongoing case. In Legal domain, recommender systems (RS) effectively identify and recommend referentially and/or semantically relevant judgments. Due to the availability of enormous amounts of judgments, RS needs to compute pairwise similarity scores for all unique judgment pairs in advance, aiming to minimize the recommendation response time. This practice introduces the scalability issue as the number of pairs to be computed increases quadratically with the number of judgments i.e., O (n2). However, there is a limited number of pairs consisting of strong relevance among the judgments. Therefore, it is insignificant to compute similarities for pairs consisting of trivial relevance between judgments. To address the scalability issue, this research proposes a graph clustering based novel Legal Document Recommendation System (LDRS) that forms clusters of referentially similar judgments and within those clusters find semantically relevant judgments. Hence, pairwise similarity scores are computed for each cluster to restrict search space within-cluster only instead of the entire corpus. Thus, the proposed LDRS severely reduces the number of similarity computations that enable large numbers of judgments to be handled. It exploits a highly scalable Louvain approach to cluster judgment citation network, and Doc2Vec to capture the semantic relevance among judgments within a cluster. The efficacy and efficiency of the proposed LDRS are evaluated and analyzed using the large real-life judgments of the Supreme Court of India. The experimental results demonstrate the encouraging performance of proposed LDRS in terms of Accuracy, F1-Scores, MCC Scores, and computational complexity, which validates the applicability for scalable recommender systems.

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