Health Recommendation System Framework for the Optimization of Medical Decisions

2020 ◽  
pp. 249-272
Author(s):  
Luis Terán ◽  
Jhonny Pincay ◽  
Diana Pacheco ◽  
Martin Štěpnička ◽  
Daniel Simancas-Racines
Keyword(s):  
Recommendation System ◽  
System Framework ◽  
Medical Decisions

scholarly journals User-Review Oriented Social Recommender System for Event Planning

2020 ◽  
Vol 25 (5) ◽  
pp. 669-675
Author(s):  
Rahul Kumar Singh ◽  
Pardeep Singh ◽  
Gourav Bathla
Keyword(s):  
Recommender System ◽  
User Satisfaction ◽  
Recommendation System ◽  
Hybrid Approach ◽  
Social Commerce ◽  
System Framework ◽  
User Comments ◽  
Filtering Technique ◽  
Event Based ◽  
User Review

Recommender system is used to suggest product or topic based on user’s interest. Existing recommender system have focused on books, product, music etc. The problem in existing recommender system is that wedding/event based suggestions are not available. In the modern information era; storage, communication has been a challenge due to information veracity, volume, and velocity. Due to the constant and exponential growth of information, the utilization of information for context-oriented services is not productive. In this paper, a wedding planner recommender system framework has been proposed based on hybrid approach i.e., content based, collaborative filtering technique. The motive of proposed framework is to generate user-specific recommendations for different tasks related to the event specially wedding event, analyzed from the user comments on his social networking portal. Its main objective is to assist the user for organizing the events by suggesting specific vendors needed to arrange the event activities. Also, it would enhance the sales of location sensitive products in social commerce. The trial study conducted using a set of Facebook users is carried out to validate the proposed recommendation system framework. The success of the proposed framework is reported in terms of the level of user satisfaction achieved.

A Novel Distributed Recommendation Framework Using Big Data in Social Context

2017 ◽  
Vol 31 (08) ◽  
pp. 1759015 ◽  
Author(s):  
Gaochao Xu ◽  
Yan Ding ◽  
Yuqiang Jiang ◽  
Ming Hu ◽  
Jia Zhao
Keyword(s):  
Big Data ◽  
Social Context ◽  
Supervised Learning ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Recommendation Systems ◽  
Computer Hardware ◽  
Personalized Recommendation ◽  
System Framework ◽  
Current Recommendation

Recently big data have become a research hotspot and been successfully exploited in a few applications such as data mining and business modeling. Although big data contain a plenty of treasures for all the fields of computer science, it is very difficult for the current computing paradigms and computer hardware to efficiently process and utilize big data to attain what are looked forward to. In this work, we explore the possibility of employing big data in recommendation systems. We have proposed a simple recommendation system framework BDRSF (Big Data Recommendation System Framework), which is based on big data with social context theories and has abilities in obtaining the Recommender based on the idea of supervised learning through big data training. Its main idea can be divided into three parts: (1) reduce the scale of the current recommendation problems according to the essence of recommending; (2) design a rational Recommender and propose a novel supervised learning algorithm to get it; (3) utilize the Recommender to deal with the later recommendation problems. Experimental results show that BDRSF outperforms conventional recommendation systems, which clearly indicates the effectiveness and efficiency of big data with social context in personalized recommendation.

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

Sign in / Sign up

Export Citation Format

Share Document