In recommender systems, cold-start issues are situations where no previous events, eg,
ratings, are known for certain users or items. In this paper, we focus on the item cold-start …

Rating prediction is a classic problem underlying recommender systems. It is traditionally
tackled with matrix factorization. Recently, deep learning based methods, especially graph …

The e-commerce era is witnessing rising new arrivals of items on e-commerce platforms
every day. Identifying potential popular items accurately is of great importance in creating …

In the last decade, collaborative ltering approaches have shown their e ectiveness in
computing accurate recommendations starting from the user-item matrix. Unfortunately, due …

Recommender systems (RSs) have become key components driving the success of e-
commerce and other platforms where revenue and customer satisfaction is dependent on …

The task of mining large unstructured text archives, extracting useful patterns and then
organizing them into a knowledgebase has attained a great attention due to its vast array of …

Previous works have shown the effectiveness of using stylistic visual features, indicative of
the movie style, in content-based movie recommendation. However, they have mainly …

Many Recommender Systems (RSs) rely on user preference data in the form of ratings or
likes for items. Previous research has shown that item comparisons can also be effectively …

With the growing popularity of e-commerce, recommender systems play a critical role to
enhance the user experience and increase sales revenue and profitability for a company …

Typically, recommendation algorithms are unable to make recommendations for new users
due to the inherent lack of information, ie, the cold start problem. To overcome this problem …