Matrix Factorization is a successful approach for generating an effective recommender
system. However, most existing matrix factorization methods suffer from the sparsity and cold …

With the rapid development of online social recommendation system, substantial methods
have been proposed. Unlike traditional recommendation system, social recommendation …

Traditional recommender systems estimate user preference on items purely based on
historical interaction records, thus failing to capture fine-grained yet dynamic user interests …

User-generated product reviews contain a lot of valuable information including users'
opinions on products and product features that is not fully exploited by the current …

Multi-scenario Learning to Rank is essential for Recommender Systems, Search Engines
and Online Advertising in e-commerce portals where the ranking models are usually applied …

Interactive recommendation with natural-language feedback can provide richer user
feedback and has demonstrated advantages over traditional recommender systems …

With the rapid growth of internet data, knowledge graphs (KGs) are considered as efficient
form of knowledge representation that captures the semantics of web objects. In recent …

Over the years, model-based approaches have shown their effectiveness in computing
recommendation lists in different domains and settings. By relying on the computation of …

Recent advances in path-based explainable recommendation systems have attracted
increasing attention thanks to the rich information from knowledge graphs. Most existing …

Purchase intentions have a great impact on future purchases and thus can be exploited for
making recommendations. However, purchase intentions are typically complex and may …