Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

One of the main current applications of Intelligent Systems are Recommender systems (RS).
RS can help users to find relevant items in huge information spaces in a personalized way …

Revealing complex relations between entities (eg, items within or between transactions) is of
great significance for business optimization, prediction, and decision making. Such relations …

In this paper, we present our work towards comparing on-line and off-line evaluation metrics
in the context of small e-commerce recommender systems. Recommending on small e …

Due to their powerful personalization and efficiency features, recommendation systems are
being used extensively in many online environments. Recommender systems provide great …

Purpose Online businesses require a deep understanding of their customers' interests to
innovate and develop new products and services. Users, on the other hand, rarely express …

Aiming at the problem of low utilization of library resources in the current network information-
flooded environment, this paper designs a book recommendation system framework suitable …

Scholarly research has extensively examined a number of issues and challenges affecting
recommender systems (eg 'cold-start','scrutability','trust','context', etc.). However, a …

Our work is generally focused on recommending for small or medium-sized e-commerce
portals, where explicit feedback is absent and thus the usage of implicit feedback is …

In this paper, we focus on the problem of rank-sensitive proportionality preservation when
aggregating outputs of multiple recommender systems in dynamic recommendation …