A substantial progress in development of new and efficient tensor factorization techniques
has led to an extensive research of their applicability in recommender systems field. Tensor …

Generative Adversarial Networks (GAN) have achieved big success in various domains
such as image generation, music generation, and natural language generation. In this …

Cold-start problems are arguably the biggest challenges faced by collaborative filtering (CF)
used in recommender systems. When few ratings are available, CF models typically fail to …

Personalized recommendation for online service systems aims to predict potential demand
by analysing user preference. User preference can be inferred from heterogeneous implicit …

The recommender system is widely used in the field of e-commerce and plays an important
role in guiding customers to make smart decisions. Although many algorithms are available …

This paper describes a new collaborative filtering recommendation algorithm based on
probability matrix factorization. The proposed algorithm decomposes the rating matrix into …

Collaborative filtering (CF) is the prevalent solution to mitigate massive information overload
in modern recommender systems. However, it usually suffers from data sparsity and …

Most online service providers utilize a recommender system to help their customers'
decision making process by producing referrals. If a customer requests a suggestion for a …

In this paper, we start by pointing out the problem of a negative sampling (NS) strategy,
denoted as nearest-NS (NNS), used in metric learning (ML)-based recommendation …

Subjective evaluation is a commonly used method in the real recognition process.
Generally, two fuzziness can be found in evaluation information, namely what values should …