Collaborative Filtering for Recommendation System (CF4RS)

Call for Papers for the Invited Session on “Collaborative Filtering for Recommendation System (CF4RS)”

Being Organized by

Professor Loc Nguyen, PhD, MD, Vietnam, Director of the International Engineering and Technology Institute (IETI), Independent Scholar at Loc Nguyen’s Academic Network

Dr. Loc Nguyen holds a postdoctoral degree in Computer Science, certified by INSTICC in 2015 and a Doctorate in Medisine. Currently, he is interested in poetry, computer science, statistics, mathematics, education, and medicine. He serves as a reviewer and editor in a wide range of international journals and conferences since 2014. He is volunteer of Statistics Without Borders since 2015. He was granted as Mathematician by London Mathematical Society for Postdoctoral research in Mathematics from 2016. He was awarded as Professor by Scientific Advances and Science Publishing Group from 2016. He was awarded Doctorate of Statistical Medicine by HOSREM in 2016. He has published 61 papers in journals, books and conference proceedings. He is author of 2 scientific books and 2 postdoctoral dissertations. He is author and creator of 6 scientific and technological products. Moreover, he is Vietnamese-language poet who has composed 1 verse story and 7 collections of 303 poems since 1993. He also has 3 music albums in which many poems are chanted by famous artists.

“Collaborative Filtering for Recommendation System (CF4RS)”

Abstract: A recommendation system is important in e-commerce when sales revenue will be increased if e-commerce websites can provide customers with favorite products (items). It is expected that customers will like and then buy these recommended products. There are two popular algorithms for recommendation such as collaborative filtering (CF) and content-based filtering (CBF). CBF recommends an item to a user if such item is similar to other items that she/he likes much in the past (her/his rating for such item is high). CF recommends an item to a user if her/his neighbors (other users similar to her/him) are interested in such item. Both of them have own strong points and drawbacks. This proposed invited session focuses on CF because CF can discover implicit items under community which can be introduced to customers. Moreover, it is not necessary to store heavy information about items that CBF requires. The proposed invited session is named “Collaborative Filtering for Recommendation System” (CF4RS). CF4RS has two goals:

    1. Hudup* framework which is a framework of e-commercial recommendation algorithms is introduced. Although Hudup framework supports both CF, CBF, and other algorithms, its strong built-in algorithms are CF algorithms. Via Hudup framework, we will prove effectiveness and feasibility of CF in recommendation system.

    2. Some new researches about CF are presented in CF4RS. Algorithms in these researches are implemented and tested on Hudup framework.

* Note, Hudup framework was finished and accepted by European Project Space ( in 2015. It was introduced at the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015), at Lisbon, Portugal, November 13, 2015. Since 2015 it has been improved many times and now it has been developed as extended Hudup framework which gives more support to remote service. Hudup framework is available at

The motivation of the invited session on “Collaborative Filtering for Recommendation System (CF4RS)” is to make scientific contributions to the area of recommendation information system and to disseminate Hudup framework.

Additional Information about CF4RS:

    • Host organization: International Institute of Informatics and Systemics (IIIS)

    • Host conference: The 11th International Multi-Conference on Complexity, Informatics and Cybernetics (IMCIC 2020)

    • Host website:

    • Host website:

    • IMCIC Theme: Computer Science and Engineering

    • IMCIC Area: Data Mining and Knowledge Presentation

    • Attendance type: Virtual

Expected Articles: up to the present, we planned sketchily to submit at least 5 papers according to requirement of IMCIC 2020 Committee, which includes 1 paper for evaluation of Hudup framework, 2 papers for similarity measure in nearest neighbor CF, 1 paper for context-based CF, 1 paper for privacy preserving in CF.

In order to enrich CF4RS with diversified researches, we accept papers on specific of following topics that are related to recommendation system area:

    • Machine learning

    • Optimization

    • Data mining

    • Adaptive system

    • Statistics

    • Applied probability

Authors submit papers by sending papers to Dr. Loc Nguyen’s email address or following the submission link

Authors will present their research by virtual participation via asynchronous, not synchronous means, i.e. according to what has been described as virtual participation at the conference in the web page

Reviewers of this Special Session are listed at


Submission starting date: November 1, 2019

Submission deadline date: January 1, 2020

Acceptance notification date: February 1, 2020

Organizing Committee Special Event

Chair: Prof. Loc Nguyen, Loc Nguyen’s Academic Network, Vietnam

Email address:,

Phone: +84-975250362


Reviewers and other members of the Organizing Committee will be selected from the Editors listed at, depending on topics of received papers.