Study on Similar Case Determination of Personalized Recommendation System

2011 ◽  
Vol 55-57 ◽  
pp. 1494-1497
Author(s):  
Jie Li Sun ◽  
Zhi Qing Zhu ◽  
Yong Mei
Keyword(s):  
Recommendation System ◽  
Similar Case ◽  
Characteristic Vector ◽  
Personalized Recommendation ◽  
Determination Method ◽  
Case Retrieval ◽  
Case Characteristic ◽  
Case Reasoning

The quality of the recommended results will depend on the determination policy of the case similarity, case retrieval policy and personalized recommended policy based on case reasoning. The case similarity determination strategy is one of the important link to design the personalized recommendation system. This paper studies the case similarity determination method of the personalized recommendation system based-CBR . And the similar determination method based on similar case characteristic vector are discussed and the relevant algorithm is given.

scholarly journals Personalized Clothing Recommendation Based on User Emotional Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xueping Su ◽  
Meng Gao ◽  
Jie Ren ◽  
Yunhong Li ◽  
Matthias Rätsch
Keyword(s):  
Facial Expression ◽  
Recommendation System ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
Continuous Development ◽  
Expression Intensity ◽  
Novel Method ◽  
Multiclass Svm

With the continuous development of economy, consumers pay more attention to the demand for personalization clothing. However, the recommendation quality of the existing clothing recommendation system is not enough to meet the user’s needs. When browsing online clothing, facial expression is the salient information to understand the user’s preference. In this paper, we propose a novel method to automatically personalize clothing recommendation based on user emotional analysis. Firstly, the facial expression is classified by multiclass SVM. Next, the user’s multi-interest value is calculated using expression intensity that is obtained by hybrid RCNN. Finally, the multi-interest value is fused to carry out personalized recommendation. The experimental results show that the proposed method achieves a significant improvement over other algorithms.

scholarly journals Simultaneous Determination of Six Active Compounds in Yixin Badiranjibuya Granules, a Traditional Chinese Medicine, by RP-HPLC-UV Method

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Ning Yu ◽  
ChenHui He ◽  
Gulistan Awuti ◽  
Cheng Zeng ◽  
JianGuo Xing ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Rosmarinic Acid ◽  
Quality Evaluation ◽  
Scientific Basis ◽  
Determination Method ◽  
Rp Hplc ◽  
And Control ◽  
First Time

In this study, a sensitive, precise, and accurate HPLC-UV method was developed and validated to simultaneously determine the six analytes (luteolin-7-O-β-D-glucuronide, apigenin-7-O-β-D-glucuronide, diosmetin-7-O-β-D-glucuronide, acacetin-7-O-β-D-glucuronide, tilianin, and rosmarinic acid) in Yixin Badiranjibuya Granules, in which five analytes (i.e., luteolin-7-O-β-D-glucuronide, apigenin-7-O-β-D-glucuronide, diosmetin-7-O-β-D-glucuronide, acacetin-7-O-β-D-glucuronide, and rosmarinic acid) were determined for the first time in Yixin Badiranjibuya Granules, the content of tilianin in Yixin Badiranjibuya Granules was reported in other literatures, and the content of tilianin in our work was higher than that of the literature reports. The quality of 11 batch samples from four different manufacturers was evaluated using the proposed determination method. The contents of the six analytes were largely different among samples from various manufacturers. Therefore, this determination method can provide a scientific basis for quality evaluation and control of Yixin Badiranjibuya Granules.

A Similarity Measure Based on Extension Distance and Its Application in CBR

2008 ◽  
Author(s):  
Yanwei Zhao ◽  
Feng Zhang ◽  
Nan Su ◽  
Huijun Tang ◽  
Jian Chen
Keyword(s):  
Standard Deviation ◽  
Similar Case ◽  
Similarity Measures ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Similarity Model ◽  
Electric Drill ◽  
Classical Distance ◽  
Case Based

Case-based reasoning (CBR) is an effective method that integrates reasoning methodology and represents related knowledge in a domain. The success of a CBR system largely depends on case retrieval, and the similarity and determination of weight for each case features have a significant influence on the efficiency and accuracy of case retrieval. The aim of the research is to improve the efficiency and accuracy of case retrieval. Analyzing the deficiency of similarity measures based on the classical distance, different similarity measures are proposed for different kinds of attribute values based on the extension distance, especially the similarity model between numerical and set considered the customer’s preference. The standard deviation related with the similarity is introduced to distribute the dynamic attribute’s weights which also considered the customer’s interest, but not the traditional methods that the weight is a constant if determined. The presented methods will enable the system to retrieve the more similar case correctly so that reducing case adaptation. In this study, an electric drill is used as a case to verify the usefulness and effectiveness of the similarity measurements and weight assignments. It is demonstrated that this method is more beneficial to case retrieval compared with other methods.

Content Personalized Recommendation Engine to Support an Informal Learning Environment in the Health Context

2016 ◽  
pp. 451-482
Author(s):  
Alisson Alan Lima da Costa ◽  
Francisco Milton Mendes Neto ◽  
Enio Lopes Sombra ◽  
Jonathan Darlan Cunegundes Moreira ◽  
Rafael Castro de Souza ◽  
...  
Keyword(s):  
Informal Learning ◽  
Chronic Diseases ◽  
Recommendation System ◽  
Health Condition ◽  
Personalized Recommendation ◽  
Web 2.0 Tools ◽  
Construction Of Knowledge ◽  
The Right ◽  
Informal Learning Environment

People with chronic diseases suffer with limitations imposed by their health condition and learn more about the disease helps in improving the quality of life. This is possible because the use in mass of mobile devices and the advent of Web 2.0 tools, which gave rise to the Health 2.0 concept. This search for the construction of knowledge by stimulating citizens to be active and responsible for their health. However, provide contextualized knowledge at the right time, it is not a trivial task due to the diversity of content and user's profiles. The solution to this is to provide informal learning through personalized recommendation of content by providing relevant content to users related to their health. This chapter proposes a personalized recommendation system of content, which includes the union of different recommendation techniques and genetic algorithm, seeking efficacy on the recommendation of the contents to people with chronic diseases aiming informal learning in health.

An Improved Personalized Recommendation System Research

2013 ◽  
Vol 756-759 ◽  
pp. 1398-1402
Author(s):  
Xing Yuan Li ◽  
Qing Shui Li
Keyword(s):  
Recommendation System ◽  
Nearest Neighbor ◽  
Information Services ◽  
Information Resources ◽  
Personalized Recommendation ◽  
System Research ◽  
Rating Data ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

In order to find information of interest and found valuable information resources in enrich Internet data. This paper describes a personalized recommendation system, personalized recommendation system is an intelligent recommendation system to help e-commerce site for customers to provide complete personalized shopping decision support and information services. for the User Rating data extreme sparseness, This paper presents nearest neighbor collaborative filtering algorithm based on project score predicted ,experiments show that this method can improve the quality of recommendation system.

A Collaborative Filtering Recommendation Algorithm Combined with User and Item

2014 ◽  
Vol 513-517 ◽  
pp. 1878-1881
Author(s):  
Feng Ming Liu ◽  
Hai Xia Li ◽  
Peng Dong
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
User Evaluation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

The collaborative filtering recommendation algorithm based on user is becoming the more personalized recommendation algorithm. But when the user evaluation for goods is very small and the user didnt evaluate the item, the commodity recommendation based on the item evaluation of user may not be accurate, and this is the sparseness in the collaborative filtering algorithm based on user. In order to solve this problem, this paper presents a collaborative filtering recommendation algorithm based on user and item. The experimental results show that this method has smaller MAE and greatly improve the quality of the recommendation in the recommendation system.

Personalized Recommendation Based on Contextual Awareness and Tensor Decomposition

2018 ◽  
Vol 16 (3) ◽  
pp. 39-51
Author(s):  
Zhenjiao Liu ◽  
Xinhua Wang ◽  
Tianlai Li ◽  
Lei Guo
Keyword(s):  
Recommendation System ◽  
Tensor Decomposition ◽  
Personalized Recommendation ◽  
Third Order ◽  
Recommendation Algorithm ◽  
Singular Value Decomposition Method ◽  
Contextual Awareness ◽  
Value Decomposition ◽  
Item Item

In order to solve users' rating sparsely problem existing in present recommender systems, this article proposes a personalized recommendation algorithm based on contextual awareness and tensor decomposition. Via this algorithm, it was first constructed two third-order tensors to represent six types of entities, including the user-user-item contexts and the item-item-user contexts. And then, this article uses a high order singular value decomposition method to mine the potential semantic association of the two third-order tensors above. Finally, the resulting tensors were combined to reach the recommendation list to respond the users' personalized query requests. Experimental results show that the proposed algorithm can effectively improve the effectiveness of the recommendation system. Especially in the case of sparse data, it can significantly improve the quality of the recommendation.

An Enhanced Similarity Measure Used in Personalized Recommendation Algorithms

2010 ◽  
Vol 159 ◽  
pp. 671-675 ◽  
Author(s):  
Song Jie Gong
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Rating Data ◽  
Measure Methods ◽  
Collaborative Filtering Algorithm

Personalized recommendation systems combine the data mining technology with users browse profile and provide recommendation set to user forecasted by their interests. Collaborative filtering algorithm is one of the most successful methods for building personalized recommendation system, and is extensively used in many fields to date. With the development of E-commerce, the magnitudes of users and items grow rapidly, resulting in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To alleviate the problem, an enhanced Pearson correlation similarity measure method is introduced in the personalized collaborative filtering recommendation algorithm. The approach considers the common correlation rating of users. The recommendation using the enhanced similarity measure can improve the neighbors influence in the course of recommendation and enhance the accuracy and the quality of recommendation systems effectively.

Tingkat Pemahaman Kompetensi Guru Pada Mahasiswa Pendidikan Geografi Sebagai Calon Guru Geografi

2020 ◽  
Vol 21 (1) ◽  
pp. 102-117
Author(s):  
Novia Zalmita ◽  
Muhajirah Muhajirah ◽  
Abdul Wahab Abdi
Keyword(s):  
Prospective Teachers ◽  
Prospective Teacher ◽  
Quantitative Description ◽  
Population Data ◽  
Geography Education ◽  
Teacher Competence ◽  
Education Department ◽  
Long Time

One that influences human resource indicators is education. The teacher is a profession as a job of academic specialization in a relatively long time in college. Understanding related to teacher competence is very important to have by a prospective teacher because it can affect the quality of performance as a professional teacher. The teacher's competence is known as pedagogic, professional, social and personality competencies. The issue in this study is how the competency of the teacher of the Department of Geography Education FKIP Unsyiah as a prospective teacher of geography? The purpose of this study was to determine the competence of teachers in the Department of Geography Education FKIP Unsyiah as prospective geography teachers. Quantitative description approach is used in this study to find answers to the issue. The population in this study were students of the Department of Geography Education FKIP Unsyiah class of 2015 and 2016 who had been declared to have passed the Micro Teaching and Magang Kependidikan 3 course totaling 50 people. Because the population is small and can be reached, the determination of the sample using total sampling techniques so that the sample in this study is the whole population. Data collection is done by distributing test questions to respondents. The data was analyzed using the descriptive statistics percentage formula. The results of the study indicate that the level of teacher competence of Geography Education Department students as prospective teachers is in the moderate category, namely as many as 22 respondents (44%). A total of 12 respondents (24%) were in the high category, 15 respondents (30%) were in the low category and 1 respondent (2%) were in the very low category.

Sign in / Sign up

Export Citation Format

Share Document