Recommender systems are very useful tools inapplication domains that suffer from information overload,offering the users suggestions they may be interested in.Owing to its business interest, e-commerce has become amajor domain in this research field, since identifying thoseproducts that the users will appreciate could increase users’consumption. However, current e-commerce recommendersystems overlook some implications of the great diversity ofproducts and services available in the market, giving rise toform fake neighborhoods in collaborative filtering strategies.In this paper, we propose applying semantic reasoning techniquesto solve this problem, thus improving, qualitativelyand computationally, the recommendation process.
['Manuela I. Martín-Vicente', 'Alberto Gil-Solla', 'Manuel Ramos-Cabrer', 'Yolanda Blanco-Fernández', 'Martín López-Nores']
Avoiding Fake Neighborhoods in e-Commerce Collaborative Recommender Systems: A Semantic Approach