scholarly journals Explicit Feedbacks Meet with Implicit Feedbacks: A Combined Approach for Recommendation System

2018 ◽  
pp. 169-181 ◽  
Author(s):  
Supriyo Mandal ◽  
Abyayananda Maiti
Keyword(s):  
Recommendation System ◽  
Combined Approach

scholarly journals Preference-Search based Recommendation System for Accommodation Facilitator : A Review

2019 ◽  
pp. 951-956
Author(s):  
Shristi Simran ◽  
Akash Pande ◽  
Payal Desai
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Online Resources ◽  
Combined Approach ◽  
Search Technique ◽  
Database Queries ◽  
Preference Model ◽  
Large Pool ◽  
Example Critiquing

Finding an online house or rented property that meet renters’ requirements is increasingly getting difficult due to large pool of choices available with the renters’ before finalizing accommodation. A renter may spend considerable amount of time exploring numerous online resources to locate accommodation that fit his requirements. Furthermore, renters may not always express their preferences in a manner that easily matches their requirements. Exploring and searching for accommodation online has been done mainly through database queries that return a list of the most suitable accommodations. Recommendation systems methods can be applied to smooth the task of finding desired and required accommodation online. This study proposes a recommendation system that enables renters to carry out a preference-based search on rental properties for accommodation. In this paper preference-based search technique is combined with method called as example-critiquing. Rather than executing a query against the database, this combined approach prompts the renters to express some preferences on rental properties for accommodation. Than this method will be used to construct a preference model for the user, and finally generates a list of properties that best match that preferences.

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.

A Modified Combined Approach to Operative Carotid and Coronary Artery Disease: 82 Cases in 8 Years

2005 ◽  
Vol 8 (3) ◽  
pp. E184-E189 ◽  
Author(s):  
Bayer Cinar ◽  
Onur S. Goksel ◽  
Sinan Kut ◽  
Veysel Sahin ◽  
Yavuz Enc ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Combined Approach ◽  
Artery Disease

Towards Small-Sized Long Tail Business with the Dual-Directed Recommendation System

2010 ◽  
Vol 130 (2) ◽  
pp. 317-323
Author(s):  
Masakazu Takahashi ◽  
Takashi Yamada ◽  
Kazuhiko Tsuda ◽  
Takao Terano
Keyword(s):  
Recommendation System ◽  
Long Tail
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