scholarly journals A basic framework for privacy protection in personalized information retrieval

2021 ◽  
Vol 33 (6) ◽  
pp. 0-0
Keyword(s):  
Information Retrieval ◽  
Information Overload ◽  
Rapid Development ◽  
User Privacy ◽  
Network Servers ◽  
Basic Framework ◽  
User Request ◽  
Privacy Model ◽  
Network Technologies ◽  
Cover Up

Personalized information retrieval is an effective tool to solve the problem of information overload. Along with the rapid development of emerging network technologies such as cloud computing, however, network servers are becoming more and more untrusted, resulting in a serious threat to user privacy of personalized information retrieval. In this paper, we propose a basic framework for the comprehensive protection of all kinds of user privacy in personalized information retrieval. Its basic idea is to construct and submit a group of well-designed dummy requests together with each user request to the server, to mix up the user requests and then cover up the user privacy behind the requests. Also, the framework includes a privacy model and its implementation algorithm. Finally, theoretical analysis and experimental evaluation demonstrate that the framework can comprehensively improve the security of all kinds of user privacy, without compromising the availability of personalized information retrieval.

E-Commerce Personalized Recommendation

2014 ◽  
Vol 989-994 ◽  
pp. 4996-4999 ◽  
Author(s):  
Yan Zhang
Keyword(s):  
Information Retrieval ◽  
Electronic Commerce ◽  
Association Rule ◽  
Information Overload ◽  
Rapid Development ◽  
Personalized Recommendation ◽  
Application Layer ◽  
Advantages And Disadvantages ◽  
Theory Research ◽  
Content Based Filtering

With the rapid development of electronic commerce, the problem of "information overload" leads to the difficulty that user can't search the required goods effectively , personalized recommendation technology has been applied in e-commerce and popularization. By using the method of qualitative analysis of the current e-commerce site, the paper compare the information retrieval, association rule, content-based filtering and collaborative filtering four main recommendation technologies and analysis the advantages and disadvantages in the application layer, the recommendation technologies are introduced to review e-commerce research hot topic in the field of personalized recommendation, and analysis the current domestic e-commerce personalized recommendation theory research and application status, finally propose the challenges faced by e-commerce personalized recommendation domain.

The Research of E-Commerce Personalized Recommendation

2014 ◽  
Vol 556-562 ◽  
pp. 6762-6765
Author(s):  
Yan Zhang ◽  
Tao Kuang
Keyword(s):  
Information Retrieval ◽  
Electronic Commerce ◽  
Association Rule ◽  
Information Overload ◽  
Rapid Development ◽  
Personalized Recommendation ◽  
Application Layer ◽  
Advantages And Disadvantages ◽  
Theory Research ◽  
Content Based Filtering

With the rapid development of electronic commerce, the problem of "information overload" leads to the difficulty that user can't search the required goods effectively , personalized recommendation technology has been applied in e-commerce and popularization. By using the method of qualitative analysis of the current e-commerce site,the paper compare the information retrieval, association rule, content-based filtering and collaborative filtering four main recommendation technologies and analysis the advantages and disadvantages in the application layer, the recommendation technologies are introduced to review e-commerce research hot topic in the field of personalized recommendation, and analysis the current domestic e-commerce personalized recommendation theory research and application status, finally propose the challenges faced by e-commerce personalized recommendation domain.

scholarly journals Image-Based Automatic Watermeter Reading under Challenging Environments

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 434
Author(s):  
Qingqi Hong ◽  
Yiwei Ding ◽  
Jinpeng Lin ◽  
Meihong Wang ◽  
Qingyang Wei ◽  
...  
Keyword(s):  
Smart Cities ◽  
Rapid Development ◽  
Mobile Network ◽  
Spatial Layout ◽  
Single Image ◽  
Automatic Instrument ◽  
Intelligent Sensors ◽  
Fifth Generation ◽  
Instrument Reading ◽  
Network Technologies

With the rapid development of artificial intelligence and fifth-generation mobile network technologies, automatic instrument reading has become an increasingly important topic for intelligent sensors in smart cities. We propose a full pipeline to automatically read watermeters based on a single image, using deep learning methods to provide new technical support for an intelligent water meter reading. To handle the various challenging environments where watermeters reside, our pipeline disentangled the task into individual subtasks based on the structures of typical watermeters. These subtasks include component localization, orientation alignment, spatial layout guidance reading, and regression-based pointer reading. The devised algorithms for orientation alignment and spatial layout guidance are tailored to improve the robustness of our neural network. We also collect images of watermeters in real scenes and build a dataset for training and evaluation. Experimental results demonstrate the effectiveness of the proposed method even under challenging environments with varying lighting, occlusions, and different orientations. Thanks to the lightweight algorithms adopted in our pipeline, the system can be easily deployed and fully automated.

A method of query expansion based on topic models and user profile for search in folksonomy

2021 ◽  
pp. 1-11
Author(s):  
Zhinan Gou ◽  
Yan Li
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Information Overload ◽  
Topic Model ◽  
User Profile ◽  
Expansion Method ◽  
Collaborative Tagging ◽  
Search Query ◽  
Tagging System ◽  
The Web

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

The Protection of User Preference Privacy in Personalized Information Retrieval: Challenges and Overviews

Libri ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Zongda Wu ◽  
Chenglang Lu ◽  
Youlin Zhao ◽  
Jian Xie ◽  
Dongdong Zou ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Privacy Protection ◽  
User Preference ◽  
User Privacy ◽  
Server Side ◽  
Algorithm Efficiency ◽  
Important Study ◽  
Basic Reference ◽  
Complete Set ◽  
Technical Features

Abstract This paper reviews a large number of research achievements relevant to user privacy protection in an untrusted network environment, and then analyzes and evaluates their application limitations in personalized information retrieval, to establish the conditional constraints that an effective approach for user preference privacy protection in personalized information retrieval should meet, thus providing a basic reference for the solution of this problem. First, based on the basic framework of a personalized information retrieval platform, we establish a complete set of constraints for user preference privacy protection in terms of security, usability, efficiency, and accuracy. Then, we comprehensively review the technical features for all kinds of popular methods for user privacy protection, and analyze their application limitations in personalized information retrieval, according to the constraints of preference privacy protection. The results show that personalized information retrieval has higher requirements for users’ privacy protection, i.e., it is required to comprehensively improve the security of users’ preference privacy on the untrusted server-side, under the precondition of not changing the platform, algorithm, efficiency, and accuracy of personalized information retrieval. However, all kinds of existing privacy methods still cannot meet the above requirements. This paper is an important study attempt to the problem of user preference privacy protection of personalized information retrieval, which can provide a basic reference and direction for the further study of the problem.

A dummy-based user privacy protection approach for text information retrieval

2020 ◽  
Vol 195 ◽  
pp. 105679
Author(s):  
Zongda Wu ◽  
Shigen Shen ◽  
Xinze Lian ◽  
Xinning Su ◽  
Enhong Chen
Keyword(s):  
Information Retrieval ◽  
Privacy Protection ◽  
User Privacy ◽  
Text Information ◽  
Text Information Retrieval

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.

Web Semantics for Personalized Information Retrieval

2017 ◽  
pp. 166-186 ◽  
Author(s):  
Aarti Singh ◽  
Anu Sharma
Keyword(s):  
Information Retrieval ◽  
Semantic Web ◽  
Web Search ◽  
Information Overload ◽  
Software Agents ◽  
Web Personalization ◽  
Comprehensive Review ◽  
Web Technologies ◽  
Intelligent Interface

This chapter explores the synergy between Semantic Web (SW) technologies and Web Personalization (WP) for demonstrating an intelligent interface for Personalized Information Retrieval (PIR) on web. Benefits of adding semantics to WP through ontologies and Software Agents (SA) has already been realized. These approaches are expected to prove useful in handling the information overload problem encountered in web search. A brief introduction to PIR process is given, followed by description of SW, ontologies and SA. A comprehensive review of existing web technologies for PIR has been presented. Although, a huge contribution by various researchers has been seen and analyzed but still there exist some gap areas where the benefits of these technologies are still to be realized in future personalized web search.

Current Network Security Systems

2005 ◽  
pp. 668-674
Author(s):  
Göran Pulkkis ◽  
Kaj Grahn ◽  
Peik Astrom
Keyword(s):  
Distributed Systems ◽  
Network Security ◽  
Communication Networks ◽  
Rapid Development ◽  
Data Communication ◽  
Security Systems ◽  
Network Servers ◽  
Security Software

Network security is defined as “a set of procedures, practices and technologies for protecting network servers, network users and their surrounding organizations” (Oppliger, 2000, Preface). The need for network security is caused by the introduction of distributed systems, networks, and facilities for data communication. Improved network security is required because of the rapid development of communication networks. Network security is achieved by using software based tools, that is, network security software (Pulkkis, Grahn & Åström, 2003).

An Improved Real-Time Recommendation for Microblogs Based on Topic

2019 ◽  
Vol 34 (02) ◽  
pp. 2059006
Author(s):  
Yikui Shi ◽  
Jiyan Liu ◽  
Lei Shi ◽  
Jianwen Zhao ◽  
Na Su
Keyword(s):  
Real Time ◽  
Information Overload ◽  
Rapid Development ◽  
Search Space ◽  
Decay Function ◽  
Recommendation Algorithm ◽  
User Similarity ◽  
User Clustering ◽  
Intermediate Variables ◽  
Over Time

With the rapid development of the Internet, people are confronted with information overload. Many recommendation methods are designed to solve this problem. The main contributions of recommendation methods proposed in this paper are as follows: (1) An improved collaborative filtering recommendation algorithm based on user clustering is proposed. Clustering is performed according to user similarity based on the user-item rating matrix. So the search space of recommendation algorithm is reduced. (2) Considering the factor that user’s interests may dynamically change over time, a time decay function is introduced. (3) A method of real-time recommendation based on topic for microblogs is designed to realize real-time recommendation effectively by preserving intermediate variables of user similarity. Experiments show that the proposed algorithms have been improved in terms of running time and accuracy.

Sign in / Sign up

Export Citation Format

Share Document