Today, recommender systems play a vital role in the acceleration of searches by internet
users to find what they are interested in. Among the strategies proposed for recommender …

Memory-based collaborative filtering is one of the recommendation system methods used to
predict a user's rating or preference by exploring historic ratings, but without incorporating …

Small adversarial perturbations of input data can drastically change the performance of
machine learning systems, thereby challenging their validity. We compare several …

Hubness is an aspect of the curse of dimensionality related to the distance concentration
effect. Hubs occur in high-dimensional data spaces as objects that are particularly often …

The hubness phenomenon is a recently discovered aspect of the curse of dimensionality.
Hub objects have a small distance to an exceptionally large number of data points while anti …

This paper studies correlation-based item-item similarity measures for recommendation
systems. While current research on recommender systems is directed toward deep learning …

For many large-scale applications in data mining, machine learning, and multimedia,
fundamental operations such as similarity search, retrieval, classification, clustering, and …

Adversarial attacks can drastically degrade performance of recommenders and other
machine learning systems, resulting in an increased demand for defence mechanisms. We …

With the arrival of big data era and the fast development of E-commerce, recommender
systems (RSs) are used in more and more application domains to assist customers in the …

It is known that memory-based collaborative filtering systems are vulnerable to shilling
attacks. In this paper, we demonstrate that hubness, which occurs in high dimensional data …