The unified profile: applying user profile data within intelligent environments

2008 ◽  
Author(s):  
C. Bailey ◽  
U. Kruschwitz ◽  
M. Gardner
Keyword(s):  
User Profile ◽  
Intelligent Environments ◽  
Profile Data

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

scholarly journals Class Based Multi Stage Encryption for Efficient Data Security in Cloud Environment Using Profile Data

2019 ◽  
Vol 1 (1) ◽  
pp. 22-29
Author(s):  
Mouleeswaran S.K. ◽  
Kanya Devi J ◽  
Illayaraja
Keyword(s):  
Data Security ◽  
User Profile ◽  
Security Measures ◽  
Profile Data ◽  
Security Issues ◽  
Multi Stage ◽  
Data Owner ◽  
Efficient Data ◽  
Number Of Classes ◽  
Different Levels

The security issues in the cloud have been well studied. The data security has much importance in point of data owner. There are number of approaches presented earlier towards performance in data security in cloud. To overcome the issues, a class based multi stage encryption algorithm is presented in this paper. The method classifies the data into number of classes and different encryption scheme is used for different classes in different levels. Similarly, the user has been authenticated for their access and they have been classified into different categories. According to the user profile, the method restricts the access of user and based on the same, the method defines security measures. A system defined encryption methodology is used for encrypting the data. Moreover, the user has been returned with other encryption methods which can be decrypted by the user using their own key provided by the system. The proposed algorithm improves the performance of security and improves the data security.

scholarly journals User Profile Correlation-Based Similarity Algorithm in Movie Recommendation System

2020 ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh Bharata Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
User Profile ◽  
Profile Data ◽  
Improve Performance ◽  
Similarity Algorithm ◽  
Single User ◽  
Previous Algorithm ◽  
Movie Recommendation

Abstract A recommendation system is a software used in the e-commerce field that provides recommendations for customers to choose the items they like. Several recommendation systems have been proposed; however, collaborative filtering is the most widely used approach. The main issue in collaborative filtering is how to implement a similarity algorithm that can improve performance in the recommendation system. Several similarity algorithms based on user rating value have been developed, and recently a similarity algorithm has been developed that combines the user rating value and the user behavior value. However, the existing research is still based only on a single user behavior value, which is the genre data. Therefore, we propose a new similarity algorithm that considers not only the genre data but also the user profile data (namely age, gender, occupation, and location). The new similarity we are proposing is called User Profile Correlation-based Similarity (UPCSim). The user profile correlation similarity was obtained by calculating the correlation coefficient between the user profile data and the user rating or behavior values. An experiment was done to compare the accuracy of the UPCSim algorithm with that of the previous algorithm. The experiment results show that the UPCSim algorithm can improve the recommendation performance MAE by 1.64% and RMSE by 1.4% compared to the previous algorithm.

A Recommender System for Learning Objects Personalized Retrieval

2012 ◽  
pp. 182-210 ◽  
Author(s):  
Ana Casali ◽  
Valeria Gerling ◽  
Claudia Deco ◽  
Cristina Bender
Keyword(s):  
Recommender System ◽  
User Profile ◽  
Learning Objects ◽  
Educational Resources ◽  
Agent Architecture ◽  
Profile Data ◽  
Personalized Search ◽  
Content Based Retrieval ◽  
Multi Agent ◽  
The Subject

This chapter describes the development of a recommender system of learning objects. This system helps a user to find educational resources that are most appropriate to his/her needs and preferences. The search is performed in different repositories of learning objects, where each object has descriptive metadata. Metadata is used to retrieve objects that satisfy not only the subject of the query, but also the user profile, taking into account his/her characteristics and preferences. A multi-agent architecture that includes several types of agents with different functionalities is used. In this chapter, we describe the modelization of the Personalized Search Agent (PS-Agent) as a graded BDI (Belief-Desire-Intention) agent. This agent is responsible for making a flexible content-based retrieval and provides an ordered list of the resources that better meet the user profile data. A prototype was implemented, and experimentation results are presented.

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