scholarly journals Efficient Mining and Recommendation of Extensive Data Through Collaborative Filtering in E-Commerce: A Survey

2018 ◽  
Vol 7 (2.24) ◽  
pp. 331
Author(s):  
N Naveen ◽  
S Ganesh Kumar
Keyword(s):  
Decision Making ◽  
Recommender System ◽  
Recommendation System ◽  
Topic Model ◽  
User Preference ◽  
The Internet ◽  
Web Page ◽  
Friend Recommendation ◽  
Decision Making System ◽  
Group Recommender

E-Commerce is the most widely used technique nowadays. Buying and selling goods on the Internet has been most admired and frequently utilized. The humongous growth of the content available on the internet has made laborious for users to search and utilize information for classifying the products. Recommendation system regarded as the best way to help the customers in buying the related products. (GRS) group recommender system aims at enhancing the customer’s benefits for buying the products. This paper summarizes the fuzzy tree matching, modeling user preference dynamics, web page recommendation, uncertainty analysis for keywords, recommender system application, temporal topic model for friend recommendation, autocratic decision-making system based on (GRS),modeling user recommender, evaluating recommender system and enhancing (GRS).  

scholarly journals Movie Recommenadation System

2021 ◽  
Vol 9 (4) ◽  
pp. 13-16
Author(s):  
Gandhali Malve ◽  
Lajree Lohar ◽  
Tanay Malviya ◽  
Shirish Sabnis
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Amount Of Information ◽  
System Recommendation ◽  
User Ratings

Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.

Aspect-Oriented Recommender Systems

2013 ◽  
pp. 55-72
Author(s):  
Punam Bedi ◽  
Sumit Kr Agarwal
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Recommender System ◽  
User Preference ◽  
Preference Learning ◽  
Decision Making Process ◽  
Design Issues ◽  
Aspect Oriented Programming ◽  
Multi Agent ◽  
Oriented Approach

Recommender systems are widely used intelligent applications which assist users in a decision-making process to choose one item amongst a potentially overwhelming set of alternative products or services. Recommender systems use the opinions of members of a community to help individuals in that community by identifying information most likely to be interesting to them or relevant to their needs. Recommender systems have various core design crosscutting issues such as: user preference learning, security, mobility, visualization, interaction etc that are required to be handled properly in order to implement an efficient, good quality and maintainable recommender system. Implementation of these crosscutting design issues of the recommender systems using conventional agent-oriented approach creates the problem of code scattering and code tangling. An Aspect-Oriented Recommender System is a multi agent system that handles core design issues of the recommender system in a better modular way by using the concepts of aspect oriented programming, which in turn improves the system reusability, maintainability, and removes the scattering and tangling problems from the recommender system.

scholarly journals Restaurant Recommendation in Vehicle Context Based on Prediction of Traffic Conditions

2021 ◽  
Vol 35 (10) ◽  
pp. 2159044
Author(s):  
Zehong Wang ◽  
Jianhua Liu ◽  
Shigen Shen ◽  
Minglu Li
Keyword(s):  
Deep Learning ◽  
Recommender System ◽  
Selection Method ◽  
User Preference ◽  
The Internet ◽  
Mobile Environment ◽  
Internet Of Vehicles ◽  
Second Stage ◽  
Traffic Conditions ◽  
Deep Learning Model

Restaurant recommendation is one of the most recommendation problems because the result of recommendation varies in different environments. Many methods have been proposed to recommend restaurants in a mobile environment by considering user preference, restaurant attributes, and location. However, there are few restaurant recommender systems according to the internet of vehicles environment. This paper presents a recommender system based on the prediction of traffic conditions in the internet of vehicles environment. This recommender system uses a phased selection method to recommend restaurants. The first stage is to screen restaurants that are on the user’s driving route; the second stage is to recommend restaurants from the user attributes, restaurant attributes (with traffic conditions), and vehicle context, using a deep learning model. The experimental evaluation shows that the proposed recommender system is both efficient and effective.

scholarly journals Sistem Pendukung Keputusan Penerangan Ruangan Berbasis IoT Menggunakan Protokol MQTT dan Fuzzy Tsukamoto

2020 ◽  
Vol 2 (2) ◽  
pp. 304-313
Author(s):  
Ahmad Fauzan Hakim ◽  
Wirarama Wedhaswara ◽  
Ahmad Zafrullah Mardiansyah
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Light Intensity ◽  
The Internet ◽  
Light Conditions ◽  
Light Bulb ◽  
Application Program ◽  
Inappropriate Use ◽  
Decision Making System ◽  
The Internet Of Things

Inappropriate use of a light bulb in light conditions in the room causes electricity to go to waste. To conserve electricity and keep the lights from breaking quickly, it needs to be done to measure the condition of the light around the lamp. For that it requires a decision-making system of the lighting room based on the Internet of things and using MQTT protocol and fuzzy tsukamoto logic methods. The MQTT protocol used is CloudMQTT to store data or be called a broker. CloudMQTT has 4 important instance info, that is server, user, password, and port. 4. That instance info is used to connect the application program with the broker in order for the system to subscribe and publish from broker to application. For fuzzy tsukamoto combination of rules built up from the three functions of membership, that is the intensity of light, time, and the condition of the light. A combination of rules from two variables is light intensity and time generates 20 combinations of rules. Deffuzification on fuzzy tsukamoto earned by taking a centralized average.

scholarly journals Decision Making System Internet Addiction in Children Aged 7-17 Year Use Forward Chaining Method (Case Study : Terik Village – Krian, Sidoarjo)

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Elfi Ratna ◽  
Ika Ratna
Keyword(s):  
Decision Making ◽  
Internet Addiction ◽  
Service Providers ◽  
The Internet ◽  
Internet Service Providers ◽  
External Application ◽  
Internet Service ◽  
Internet Users ◽  
Decision Making System

Based on a survey conducted by the APJII (Indonesian Internet Service Providers Association) team in 2018 with a percentage of more than 64.8% using the internet. The highest percentage is at the age of 7-19 years, which is 91%, this shows that the number of internet users is getting higher every year. This study aims to help treat children who are addicted to the Internet and as advice or a reference for making decisions on how to deal with people who are addicted to the Internet. The method used was to collect data on the distribution of questionnaires or questions that were carried out directly on children aged 7-17 years in the blistering village. The results of external application testing with Likert scale calculations get a percentage result of 78.36% (very useful).

scholarly journals Decision Making in Internet of Things (IoT) : A Systematic Literature Review

2020 ◽  
Vol 5 (1) ◽  
pp. 51-65
Author(s):  
Hespri Yomeldi
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Research Question ◽  
Fog Computing ◽  
The Internet ◽  
New Paradigm ◽  
Decision Making Factors ◽  
Decision Making System ◽  
Machine Communication ◽  
Support Decision Making

Today’s internet technologies support everything that human do. By using integrated technologies the things that connected to internet can provide data. The Internet of Things (IoT) is the new paradigm in provide the data without human communicated. The IoT system support machine to machine communication that can be used to develop smart services that can generate a lot of data. This exponential data can support a decision making. The decision making system depend on availability and reliability of data. This study focus to how the Internet of Thing support decision making system. With a survey of literature to understand the trends, models and factors of decision making in IoT based on previous research. This survey following step by conduct the research question (RQ), then search and observation the previous research from database journal. Based on reviewing 26 articles, this study conclude that the trends of decision making in IoT are implemented on Manufacturing and Industry, Healthcare, Agriculture and Transportation. Besides that the decision model that can support by IoT used Fog Computing,  Fuzzy, Game Theoritic, Clustering Based on Multimodal Data Correlation, etc. Meanwhile the decision making factors that influenced by IoT like Latency, data-driven, security, data reliability and accurate.  The integrated of model and point of interest on decision making in IoT should be improved.  It will be the opportunities and challenge in IoT to support decision making in future.

Real-Time Recommendation Engine for Readers

2021 ◽  
pp. 165-177
Author(s):  
Sangeeta Namdev Dhamdhere ◽  
Deepak Mane
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Real Time ◽  
Recommendation System ◽  
The Internet ◽  
Problem Analysis ◽  
Social Media Data ◽  
Good Book ◽  
Data Ingestion ◽  
Media Data

In today's world, every reader or social media user has different choices/hobbies in terms of reading. For example, if any social media user is searching for a book to read without any specific idea of what s/he wants, s/he wastes a lot of time browsing around on the internet and crawling/trawling through various sites hoping that s/he might get good book. To avoid confusion, the authors are building a recommendation system for every reader/user that helps to recommend a book based on his choices, hobbies, or what s/he had read previously that will be massive help for users instead wasting time on various sites. Data from social media is the powerful fuel that can be used to helps in decision making and building a recommendation engine. Social media data in the different format is biggest challenge for the business to ingest data at the reasonable speed and further process. In social media data, it is difficult to detect and capture data. Real-time recommendation engine for users, which includes data ingestion methods, challenges, metadata problem, analysis, and consumption, is discussed here.

scholarly journals An Interactive Recommendation System for Decision Making Based on the Characterization of Cognitive Tasks

2021 ◽  
Vol 26 (2) ◽  
pp. 35
Author(s):  
Teodoro Macias-Escobar ◽  
Laura Cruz-Reyes ◽  
César Medina-Trejo ◽  
Claudia Gómez-Santillán ◽  
Nelson Rangel-Valdez ◽  
...  
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Recommender System ◽  
Recommendation System ◽  
Optimization Problems ◽  
Real Life ◽  
Argumentation Theory ◽  
Cognitive Tasks ◽  
Decision Aiding

The decision-making process can be complex and underestimated, where mismanagement could lead to poor results and excessive spending. This situation appears in highly complex multi-criteria problems such as the project portfolio selection (PPS) problem. Therefore, a recommender system becomes crucial to guide the solution search process. To our knowledge, most recommender systems that use argumentation theory are not proposed for multi-criteria optimization problems. Besides, most of the current recommender systems focused on PPS problems do not attempt to justify their recommendations. This work studies the characterization of cognitive tasks involved in the decision-aiding process to propose a framework for the Decision Aid Interactive Recommender System (DAIRS). The proposed system focuses on a user-system interaction that guides the search towards the best solution considering a decision-maker’s preferences. The developed framework uses argumentation theory supported by argumentation schemes, dialogue games, proof standards, and two state transition diagrams (STD) to generate and explain its recommendations to the user. This work presents a prototype of DAIRS to evaluate the user experience on multiple real-life case simulations through a usability measurement. The prototype and both STDs received a satisfying score and mostly overall acceptance by the test users.

scholarly journals Recommendation System: Techniques and Issues

2019 ◽  
Vol 8 (3) ◽  
pp. 2821-2824
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Information Overload ◽  
Daily Life ◽  
Hybrid Approach ◽  
The Internet ◽  
Individual Interest ◽  
The Many ◽  
Content Based Filtering

In daily life user searched the many things over the internet on the basis of requirement with the help of search engines. Recommendation systems are widely used on the internet to help the user in discover the products or services that are best with their individual interest. RS effectively reduce the information overload by providing personalized suggestions to user when searching for items like movies, songs, or books etc. The main aim of RS is to help the users by providing the surface of information that relevant to them, fulfill their needs and their task. The paper provides an overview of RS and analyze the different approaches used for develop RS that include collaborative filtering, content-based filtering and hybrid approach of recommender system.

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