To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …

Recommender systems aim to provide item recommendations for users and are usually
faced with data sparsity problems (eg, cold start) in real-world scenarios. Recently pre …

Recommender systems become essential with the presence of the internet and social
media. The perceived benefits of the recommender system can make it easier for users to …

The widespread use of online recruitment services has led to an information explosion in the
job market. As a result, recruiters have to seek intelligent ways for Person-Job Fit, which is …

In the realm of deep learning-based recommendation systems, the increasing computational
demands, driven by the growing number of users and items, pose a significant challenge to …

Job interviews are the most widely accepted method for companies to select suitable
candidates, and a critical challenge is finding the right questions to ask job candidates …

Research on recommendation systems is swiftly producing an abundance of novel methods,
constantly challenging the current state-of-the-art. Inspired by advancements in many …

Recent advances in Large Language Models (LLMs) have been changing the paradigm of
Recommender Systems (RS). However, when items in the recommendation scenarios …

Most recommender systems optimize the model on observed interaction data, which is
affected by the previous exposure mechanism and exhibits many biases like popularity bias …

Recently, one critical issue looms large in the field of recommender systems–there are no
effective benchmarks for rigorous evaluation–which consequently leads to unreproducible …