scholarly journals Travel Data Sequence from Multi-Source Recommendation System

2018 ◽  
Vol 7 (4.6) ◽  
pp. 82
Author(s):  
Ms. Shobarani ◽  
Dr. Anandam Velagandula ◽  
Mr. Ravula Arun Kumar ◽  
B Anandkumar
Keyword(s):  
Recommendation System ◽  
Personalized Recommendation ◽  
Mobility Patterns ◽  
Data Sequence ◽  
Query Image ◽  
Road Capacity ◽  
Interest Level ◽  
Similar Images ◽  
Unobserved Preferences ◽  
Source Limitation

Due to different sort of preferences and restrictions of a trip such as time source limitation and every tourist’s destination points the travel based recommendation has become a challenging task. Most importantly the data generated by the geo-tagged social channel from the geo based tag tweets, snapshots of credentials.  Due to examining this, extended data allows us to invent the profiles, daily mobility patterns, and results of the user’s. To resolve the issues and challenges of capacity providing their personalized and sequential travel to make package recommendation to a topical package model and to take using social media info in which mechanically mine person travel interest with another quality like time, cost, and period of wayfaring. Here, we had a proposal that a travel data sequence after a multi source recommendation system. We implemented a location recommendation system that derives personal preferences while accounting for restraints irremissibly by road capacity in order to change the demand of travel. We first infer unobserved preferences using a machine learning technique from data mining records. It extends our method to provide personalized suggestions based on user geo co-ordinates points. By utilizing the tree based hierarchal graphs (TBHG), location histories of the multiple users’ have been modeled.  In order to collect the selected places interest level and travel knowledge of user’s, the HITS model had developed based on TBHG. Finally, hybrid filtering approach based on HITS is utilized to get the global positioning system (GPS) based personalized recommendation system. And for image based search similar images with the tag information are retrieved for the query image users. 

Personalized Content Recommendation Engine for Web Publishing Services Using Textmining and Predictive Analytics

2017 ◽  
pp. 113-124
Author(s):  
Başar Öztayşi ◽  
Ahmet Tezcan Tekin ◽  
Cansu Özdikicioğlu ◽  
Kerim Caner Tümkaya
Keyword(s):  
Recommendation System ◽  
Predictive Analytics ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Web Contents ◽  
Interest Level ◽  
History Of ◽  
Content Recommendation ◽  
Publishing Services ◽  
Web Publishing

Recommendation systems have become very important especially for internet based business such as e-commerce and web publishing. While content based filtering and collaborative filtering are most commonly used groups in recommendation systems there are still researches for new approaches. In this study, a personalized recommendation system based on text mining and predictive analytics is proposed for a real world web publishing company. The approach given in this chapter first preprocesses existing web contents, integrate the structured data with history of a specific user and create an extended TDM for the user. Then this data is used for prediction of the users interest in new content. In order to reach that point, SVM, K-NN and Naïve Bayesian methods are used. Finally, the best performing method is used for determining the interest level of the user in a new content. Based on the forecasted interest levels the system recommends among the alternatives.

A Personalized Recommendation Model in E Commerce Based on TOPSIS Algorithm

2014 ◽  
Vol 12 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Liang Wang ◽  
Runtong Zhang ◽  
Huan Ruan
Keyword(s):  
Recommendation System ◽  
Personalized Recommendation ◽  
User Evaluation ◽  
Evaluation Indexes ◽  
Structured Products ◽  
Digital Books ◽  
Improved Model

From the perspective of performance and universality, this paper analyzed the characteristics of typical technologies for personalized recommendation system, and then made a basic architecture for the improved model. With the architecture, this paper introduced a personalized recommendation model in e-commerce system. The model is based on an n-tiers structure and the TOPSIS algorithm, first standardize the user evaluation indexes, and then determine the indexes weights according to user's needs, and finally calculate the personalized recommendation results. This model can be applied to a variety of e-commerce applications, especially for the e-commerce application with structured or semi-structured products such as digital books, journals and other publications.

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.

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.

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