Published In : IJCSN Journal Volume 6, Issue 5

Date of Publication : October2017

Pages : 625-634

Figures :04

Tables : 02

 

Solomon Demissie Seifu : received a bachelor degree in the field of computer science and information technology with distinction in 2006 from Adama Science and Technology University (Ethiopia), then he hold a Masters degree in the field of Information Science in 2010 from Addis Ababa University (Ethiopia), and in 2015 he again received a Master of Technology (M.Tech) degree in Computer Science and Technology from Andhra University, India. He has published 2 papers in international Journals. He worked as Lecturer at the University of Debre Berhan (Ethiopia). His field of teaching includes programming languages like C, C++, Java etc, networking, Database and operating systems. His research interests include data mining and machine learning, recommender systems, cloud computing, and software engineering.

M. Shashi : is a Professor and Chairperson of Board of Studies of the Department of Computer Science & Systems Engineering, A.U. College of Engineering(A), Andhra University, Visakhapatnam, Andhra Pradesh. She received the AICTE Career Award in 1996, Best Ph.D thesis prize from Andhra University in the year 1994 and AP State Best teacher award in 2016. 13 Ph.D.’s were awarded under her guidance. She co-authored more than 60 technical research papers in International Journals and 50 International Conferences and delivered many invited talks in such academic events. She is a member of IEEE Computational Intelligence group, Fellow of Institute of Engineers (India) and life member of Computer Society of India.. Her current research interests include Data warehousing and Mining, Data Analytics, Artificial Intelligence, Soft Computing and Machine Learning.

 

Collaborative Filtering (CF); context; context-based recommendation; context-aware rating prediction

In this paper, we demonstrated the potential of using user’s contextual information and analyzing the use of a contextual tag to improve the prediction accuracy and enhance its quality. Through our experiment we showed how we explore the latent preferences based on the user, item, and context dimensions; i.e., latent user’s preferences towards contexts, latent contexts preferences towards items, as well as latent user’s preferences towards items. Additionally, we demonstrated a prediction performance improvement on latent preference models via optimization process.

 

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