Title: ENGINEERING MINING A LARGE SCALE DATA BASED ON FEATURE ENGINEERING, METADATA, AND ONTOLOGIES

Issue Number: Vol. 6, No. 4
Year of Publication: Oct - 2016
Page Numbers: 219-229
Authors: Ahmed Adeeb Jalal, Oğuz Altun
Journal Name: International Journal of Digital Information and Wireless Communications (IJDIWC)
- Hong Kong
DOI:  http://dx.doi.org/10.17781/P002091

Abstract:


The growth of web especially in a social network in a continuously increasing. Multiplicity of offered items such as products or web pages, has made pick up relevant items for a user which searching for it a tedious. On the other hand, different tastes and behaviors of users is making likelihood to finding a neighbor user hard to get. Therefore, difficult for automated software systems to discover what is interesting to users. We have proposed a new approach to adapt to this widespread in e-commerce nowadays to reduce multiplicity impact of items and different views of users that can quickly produce the recommendations. We will exploit the domain knowledge of training data set to creating testing data set depending on an attribute of one feature that represents distinctive item genre. The testing data set will be the inputs to a hybrid recommender systems which is aspiring to achieve best recommendations through performing meta-level hybridization techniques that combine of content-based recommender systems and collaborative recommender systems. The proposed approach will reduce from effects of sparsity, cold start, and scalability very common problems with the collaborative recommender systems. Additionally to, improve the recommendations accuracy comparing with the pure collaborative filtering Pearson Correlation approach.