scholarly journals Edge and Cloud Collaborative Entity Recommendation Method towards the IoT Search

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1918 ◽  
Author(s):  
Ruyan Wang ◽  
Yuzhe Liu ◽  
Puning Zhang ◽  
Xuefang Li ◽  
Xuyuan Kang
Keyword(s):  
Real Time ◽  
Information Search ◽  
Recommendation System ◽  
Search Behavior ◽  
User Interest ◽  
State Information ◽  
The Real ◽  
Clustering Model ◽  
Entity Identification ◽  
User Search Behavior

There are massive entities with strong denaturation of state in the physical world, and users have urgent needs for real-time and intelligent acquisition of entity information, thus recommendation technologies that can actively provide instant and precise entity state information come into being. Existing IoT data recommendation methods ignore the characteristics of IoT data and user search behavior; thus the recommendation performances are relatively limited. Considering the time-varying characteristics of the IoT entity state and the characteristics of user search behavior, an edge-cloud collaborative entity recommendation method is proposed via combining the advantages of edge computing and cloud computing. First, an entity recommendation system architecture based on the collaboration between edge and cloud is designed. Then, an entity identification method suitable for edge is presented, which takes into account the feature information of entities and carries out effective entity identification based on the deep clustering model, so as to improve the real-time and accuracy of entity state information search. Furthermore, an interest group division method applied in cloud is devised, which fully considers user’s potential search needs and divides user interest groups based on clustering model for enhancing the quality of recommendation system. Simulation results demonstrate that the proposed recommendation method can effectively improve the real-time and accuracy performance of entity recommendation in comparison with traditional methods.

scholarly journals Phone-Based Restaurant Recommendation System by Using Position Information and Association Rules

2012 ◽  
Vol 6-7 ◽  
pp. 783-789
Author(s):  
Jian Feng Dong ◽  
Tian Yang Dong ◽  
Jia Jie Yao ◽  
Ling Zhang
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Association Rules ◽  
Recommendation System ◽  
Rapid Development ◽  
Smart Phone ◽  
Smart Phones ◽  
Location Information ◽  
Position Information ◽  
The Real

With the rapid development of smart-phone applications, how to make the ordering process via smart-phones more convenient and intelligent has become a hotspot. This paper puts forward a method of restaurant dish recommendation relying on position information and association rules. In addition, this paper has designed and developed a restaurant recommendation system based on mobile phone. The system would fetch the real-time location information via smart-phones, and provide customers personalized restaurant and dish recommendation service. According to the related applications, this system can successfully recommend the related restaurants and food information to customers.

scholarly journals Optimization of an Intelligent Music-Playing System Based on Network Communication

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liaoyan Zhang
Keyword(s):  
Real Time ◽  
Recommendation System ◽  
Topic Model ◽  
User Interaction ◽  
Interaction Data ◽  
Music Recommendation ◽  
System Testing ◽  
The Real ◽  
Wide Range ◽  
And Performance

Streaming media server is the core system of audio and video application in the Internet; it has a wide range of applications in music recommendation. As song libraries and users of music websites and APPs continue to increase, user interaction data are generated at an increasingly fast rate, making the shortcomings of the original offline recommendation system and the advantages of the real-time streaming recommendation system more and more obvious. This paper describes in detail the working methods and contents of each stage of the real-time streaming music recommendation system, including requirement analysis, overall design, implementation of each module of the system, and system testing and analysis, from a practical scenario. Moreover, this paper analyzes the current research status and deficiencies in the field of music recommendation by analyzing the user interaction data of real music websites. From the actual requirements of the system, the functional and performance goals of the system are proposed to address these deficiencies, and then the functional structure, general architecture, and database model of the system are designed, and how to interact with the server side and the client side is investigated. For the implementation of data collection and statistics module, this paper adopts Flume and Kafka to collect user behavior data and uses Spark Streaming and Redis to count music popularity trends and support efficient query. The recommendation engine module in this paper is designed and optimized using Spark to implement incremental matrix decomposition on data streams, online collaborative topic model, and improved item-based collaborative filtering algorithm. In the system testing section, the functionality and performance of the system are tested, and the recommendation engine is tested with real datasets to show the discovered music themes and analyze the test results in detail.

Clustering Users According to Common Interest Based on User Search Behavior

2010 ◽  
Vol 143-144 ◽  
pp. 851-855 ◽  
Author(s):  
Pei Ying Zhang ◽  
Ya Jun Du ◽  
Chang Wang
Keyword(s):  
Recommendation System ◽  
Search Behavior ◽  
Common Interest ◽  
Web Pages ◽  
User Session ◽  
Novel Method ◽  
The Common ◽  
Syntactic Similarity ◽  
User Search Behavior ◽  
The Web

The paper presents a novel method to cluster users who share the common interest and discover their common interest domain by mining different users’ search behaviors in the user session, mainly the consecutive search behavior and the click sequence considering the click order and the syntactic similarity. The community is generated and this information will be used in the recommendation system in the future. Also the method is ‘content-ignorant’ to avoid the storage and manipulation of a large amount of data when clustering the web pages by content. The experiment proved it an available and effective way.

The Real-Time Neural Genesis of a Punchline

2014 ◽  
Author(s):  
Irving Biederman ◽  
Ori Amir
Keyword(s):  
Real Time ◽  
The Real

Real-Time System for Sending Data from Three Clients to One Server with MATLAB

2015 ◽  
Vol 2 (1) ◽  
pp. 35-41
Author(s):  
Rivan Risdaryanto ◽  
Houtman P. Siregar ◽  
Dedy Loebis
Keyword(s):  
Information System ◽  
Real Time ◽  
Time System ◽  
Real Time System ◽  
The Real ◽  
Performance Time ◽  
Data Process ◽  
Effectiveness And Efficiency

The real-time system is now used on many fields, such as telecommunication, military, information system, evenmedical to get information quickly, on time and accurate. Needless to say, a real-time system will always considerthe performance time. In our application, we define the time target/deadline, so that the system should execute thewhole tasks under predefined deadline. However, if the system failed to finish the tasks, it will lead to fatal failure.In other words, if the system cannot be executed on time, it will affect the subsequent tasks. In this paper, wepropose a real-time system for sending data to find effectiveness and efficiency. Sending data process will beconstructed in MATLAB and sending data process has a time target as when data will send.

The real-time computing of the Stirling formula based on hardware acceleration

2013 ◽  
Author(s):  
Jiyang Yu ◽  
Dan Huang ◽  
Siyang Zhao ◽  
Nan Pei ◽  
Huixia Cheng ◽  
...  
Keyword(s):  
Real Time ◽  
Hardware Acceleration ◽  
Stirling Formula ◽  
The Real ◽  
Real Time Computing
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