scholarly journals “SeoulHouse2Vec”: An Embedding-Based Collaborative Filtering Housing Recommender System for Analyzing Housing Preference

2020 ◽  
Vol 12 (17) ◽  
pp. 6964 ◽  
Author(s):  
Han Jong Jun ◽  
Jae Hee Kim ◽  
Deuk Young Rhee ◽  
Sun Woo Chang
Keyword(s):  
Decision Making ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Individual Investors ◽  
Relative Preference ◽  
Trade Offs ◽  
Housing Preference ◽  
Administrative Units ◽  
Architectural Planning ◽  
Demographic Attributes

Housing preference is the subjective and relative preference of users toward housing alternatives and studies in the field have been conducted to analyze the housing preferences of groups with sharing the same socio-demographic attributes. However, previous studies may not suggest the preference of individuals. In this regard, this study proposes “SeoulHouse2Vec,” an embedding-based collaborative filtering housing recommendation system for analyzing atypical and nonlinear housing preference of individuals. The model maps users and items in each dense vector space which are called embedding layers. This model may reflect trade-offs between the alternatives and recommend unexpected housing items and thus improve rational housing decision-making. The model expanded the search scope of housing alternatives to the entire city of Seoul utilizing public big data and GIS data. The preferences derived from the results can be used by suppliers, individual investors, and policymakers. Especially for architects, the architectural planning and design process will reflect users’ perspective and preferences, and provide quantitative data in the housing decision-making process for urban planning and administrative units.

scholarly journals Movie Recommended System by Using Collaborative Filtering

2019 ◽  
pp. 9-15
Author(s):  
Bheema Shireesha ◽  
Navuluri Madhavilatha ◽  
Chunduru Anilkumar
Keyword(s):  
Decision Making ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Large Size

Recommendation system helps people in decision making an item/person. Recommender systems are now pervasive and seek to make profit out of customers or successfully meet their needs. Companies like Amazon use their huge amounts of data to give recommendations for users. Based on similarities among items, systems can give predictions for a new item’s rating. Recommender systems use the user, item, and ratings information to predict how other users will like a particular item. In this project, we attempt to under- stand the different kinds of recommendation systems and compare their performance on the Movie Lens dataset. Due to large size of data, recommendation system suffers from scalability problem. Hadoop is one of the solutions for this problem.

Segments Influencing Individual Investors Behavior And Their Investment Decision

2019 ◽  
Vol 118 (9) ◽  
pp. 154-160
Author(s):  
Dr. Kartikey Koti
Keyword(s):  
Decision Making ◽  
Social Factors ◽  
Stock Markets ◽  
Investment Decision ◽  
Individual Investors ◽  
Factors Affecting ◽  
The Social ◽  
Karnataka State ◽  
Essential Idea

The essential idea of this assessment is investigate the social factors affecting particular theorists' decisions making limit at Indian Stock Markets. In the examination coordinated standard of direct is Classified subject to two estimations the first is Heuristic (Decision making) and the resulting one is prospect.. For the assessment coordinated the data used is basic natured which is assembled through a sorted out survey from 100 individual money related authorities based out in Hubli and Dharwad city, Karnataka State in India on an accommodating way. The respondents were both sex and overwhelming part male were 68% . These theorists were having a spot with the age bundle between35-45 which is 38%. These respondents have completed their graduation were around 56%. These respondents had work inclusion of 5 to 10 years which is 45% and the majority of which were used in government portion which is 56%. Their compensation was between 4 to 6 Lakh and were fit for placing assets into business areas. The money related experts were widely masterminded placing assets into different portfolios like 32% in Share market and 20 % in Fixed store. These examiners mode to known various endeavor streets were through News, family and allies.  

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

An Analysis of the Demand for Reporting on Internal Control

2000 ◽  
Vol 14 (3) ◽  
pp. 325-341 ◽  
Author(s):  
Heather M. Hermanson
Keyword(s):  
Decision Making ◽  
Financial Reporting ◽  
Internal Control ◽  
Operational Definition ◽  
Financial Statement ◽  
Individual Investors ◽  
Internal Auditors ◽  
Additional Information ◽  
User Groups ◽  
Definition Of

The purpose of this study is to analyze the demand for reporting on internal control. Nine financial statement user groups were identified and surveyed to determine whether they agree that: (1) management reports on internal control (MRIC) are useful, (2) MRICs influence decisions, and (3) financial reporting is improved by adding MRICs. In addition, the paper examined whether responses varied based on: (1) the definition of internal control used (manipulated as broad, operational definition vs. narrow, financial-reporting definition) and (2) user group. The results indicate that financial statement users agree that internal controls are important. Respondents agreed that voluntary MRICs improved controls and provided additional information for decision making. Respondents also agreed that mandatory MRICs improved controls, but did not agree about their value for decision making. Using a broad definition of controls, respondents strongly agreed that MRICs improved controls and provided a better indicator of a company's long-term viability. Executive respondents were less likely to agree about the value of MRICs than individual investors and internal auditors.

Renewable energy selection based on a new entropy and dissimilarity measure on an interval-valued neutrosophic set

2021 ◽  
pp. 1-18
Author(s):  
ShuoYan Chou ◽  
Truong ThiThuy Duong ◽  
Nguyen Xuan Thao
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Membership Function ◽  
Fossil Fuels ◽  
Multiple Criteria Decision Making ◽  
Clean Energy ◽  
Dissimilarity Measure ◽  
Neutrosophic Set ◽  
Trade Offs ◽  
And Performance

Energy plays a central part in economic development, yet alongside fossil fuels bring vast environmental impact. In recent years, renewable energy has gradually become a viable source for clean energy to alleviate and decouple with a negative connotation. Different types of renewable energy are not without trade-offs beyond costs and performance. Multiple-criteria decision-making (MCDM) has become one of the most prominent tools in making decisions with multiple conflicting criteria existing in many complex real-world problems. Information obtained for decision making may be ambiguous or uncertain. Neutrosophic is an extension of fuzzy set types with three membership functions: truth membership function, falsity membership function and indeterminacy membership function. It is a useful tool when dealing with uncertainty issues. Entropy measures the uncertainty of information under neutrosophic circumstances which can be used to identify the weights of criteria in MCDM model. Meanwhile, the dissimilarity measure is useful in dealing with the ranking of alternatives in term of distance. This article proposes to build a new entropy and dissimilarity measure as well as to construct a novel MCDM model based on them to improve the inclusiveness of the perspectives for decision making. In this paper, we also give out a case study of using this model through the process of a renewable energy selection scenario in Taiwan performed and assessed.

scholarly journals Extrapolative Collaborative Filtering Recommendation System with Word2Vec for Purchased Product for SMEs

2021 ◽  
Vol 13 (13) ◽  
pp. 7156
Author(s):  
Kyoung Jun Lee ◽  
Yu Jeong Hwangbo ◽  
Baek Jeong ◽  
Ji Woong Yoo ◽  
Kyung Yang Park
Keyword(s):  
Collaborative Filtering ◽  
High Performance ◽  
Recommendation System ◽  
Small And Medium Enterprises ◽  
Personal Information ◽  
Business Environment ◽  
Share Data ◽  
Artificial Intelligence Technology ◽  
Medium Enterprises ◽  
Start Ups

Many small and medium enterprises (SMEs) want to introduce recommendation services to boost sales, but they need to have sufficient amounts of data to introduce these recommendation services. This study proposes an extrapolative collaborative filtering (ECF) system that does not directly share data among SMEs but improves recommendation performance for small and medium-sized companies that lack data through the extrapolation of data, which can provide a magical experience to users. Previously, recommendations were made utilizing only data generated by the merchant itself, so it was impossible to recommend goods to new users. However, our ECF system provides appropriate recommendations to new users as well as existing users based on privacy-preserved payment transaction data. To accomplish this, PP2Vec using Word2Vec was developed by utilizing purchase information only, excluding personal information from payment company data. We then compared the performances of single-merchant models and multi-merchant models. For the merchants with more data than SMEs, the performance of the single-merchant model was higher, while for the SME merchants with fewer data, the multi-merchant model’s performance was higher. The ECF System proposed in this study is more suitable for the real-world business environment because it does not directly share data among companies. Our study shows that AI (artificial intelligence) technology can contribute to the sustainability and viability of economic systems by providing high-performance recommendation capability, especially for small and medium-sized enterprises and start-ups.

scholarly journals Using Deliberative Democracy for Better Urban Decision-Making through Integrative Thinking

Urban Science ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 3
Author(s):  
Janette Hartz-Karp ◽  
Dora Marinova
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Deliberative Democracy ◽  
Decision Makers ◽  
Capital City ◽  
City Region ◽  
Multiple Sources ◽  
Trade Offs ◽  
Urban Issues ◽  
Sustainability Challenges

This article expands the evidence about integrative thinking by analyzing two case studies that applied the collaborative decision-making method of deliberative democracy which encourages representative, deliberative and influential public participation. The four-year case studies took place in Western Australia, (1) in the capital city Perth and surrounds, and (2) in the city-region of Greater Geraldton. Both aimed at resolving complex and wicked urban sustainability challenges as they arose. The analysis suggests that a new way of thinking, namely integrative thinking, emerged during the deliberations to produce operative outcomes for decision-makers. Building on theory and research demonstrating that deliberative designs lead to improved reasoning about complex issues, the two case studies show that through discourse based on deliberative norms, participants developed different mindsets, remaining open-minded, intuitive and representative of ordinary people’s basic common sense. This spontaneous appearance of integrative thinking enabled sound decision-making about complex and wicked sustainability-related urban issues. In both case studies, the participants exhibited all characteristics of integrative thinking to produce outcomes for decision-makers: salience—grasping the problems’ multiple aspects; causality—identifying multiple sources of impacts; sequencing—keeping the whole in view while focusing on specific aspects; and resolution—discovering novel ways that avoided bad choice trade-offs.

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