A Channel Recommendation System in Mobile Environment

2006 ◽  
Author(s):  
Sungjoon Park ◽  
Sanggil Kang ◽  
Young-Kuk Kim
Keyword(s):  
Recommendation System ◽  
Mobile Environment

A channel recommendation system in mobile environment

2006 ◽  
Vol 52 (1) ◽  
pp. 33-39 ◽  
Author(s):  
Sungjoon Park ◽  
Sanggil Kang ◽  
Young-Kuk Kim
Keyword(s):  
Recommendation System ◽  
Mobile Environment

Mobile Information Recommendation Using Multi-Criteria Decision Making with Bayesian Network

2015 ◽  
Vol 14 (02) ◽  
pp. 317-338 ◽  
Author(s):  
Han-Saem Park ◽  
Moon-Hee Park ◽  
Sung-Bae Cho
Keyword(s):  
Decision Making ◽  
Bayesian Network ◽  
Recommendation System ◽  
Multi Criteria Decision Making ◽  
Group Recommendation ◽  
Mobile Environment ◽  
Subjective Test ◽  
Reliable Performance ◽  
Information Recommendation ◽  
Hierarchy Process

The advancement of network technology and the popularization of the Internet lead to increased interest in information recommendation. This paper proposes a group recommendation system that takes the preferences of group users in mobile environment and applies the system to recommendation of restaurants. The proposed system recommends the restaurants by considering various preferences of multiple users. To cope with the uncertainty in mobile environment, we exploit Bayesian network, which provides reliable performance and models individual user's preference. Also, Analytical Hierarchy Process of multi-criteria decision-making method is used to estimate the group users' preference from individual users' preferences. Experiments in 10 different situations provide a comparison of the proposed method with random recommendation, simple rule-based recommendation and neural network recommendation, and confirm that the proposed method is useful with the subjective test.

scholarly journals A Comparison Between AHP and Hybrid AHP for Mobile Based Culinary Recommendation System

2018 ◽  
Vol 12 (1) ◽  
pp. 133 ◽  
Author(s):  
Ratih Kartika Dewi ◽  
Buce Trias Hanggara ◽  
Aryo Pinandito
Keyword(s):  
Mobile Application ◽  
Time Complexity ◽  
Recommendation System ◽  
Fuzzy Ahp ◽  
Ideal Solution ◽  
Order Of Growth ◽  
Mobile Environment ◽  
Analytic Hierarchy ◽  
Topsis Technique ◽  
Hierarchy Process

Mobile based culinary recommendation system has become critical topic in mobile application. Some methods presented in the literature propose the use of the AHP (Analytic Hierarchy Process), AHP TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and fuzzy AHP for mobile based culinary recommendation system. However, there are no comparative studies of these three methods when applied to mobile based culinary recommendation system. Thus, this research presents a comparative analysis of these three methods in the context of culinary recommendation system in mobile environment. The comparison was made based on accuracy and time complexity because mobile application environment needs low time complexity. The results have shown that all of these methods are suitable for culinary recommendation system in mobile environment. Fuzzy AHP have the highest accuracy between all of these methods, it have 66,67 % accuracy. But, AHP TOPSIS shows the best performance in time complexity, with order of growth in quadratic class (n2)

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.

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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