Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …

Sequential dynamics are a key feature of many modern recommender systems, which seek
to capture the'context'of users' activities on the basis of actions they have performed recently …

Fashion recommendation is a key research field in computational fashion research and has
attracted considerable interest in the computer vision, multimedia, and information retrieval …

To learn a sequential recommender, the existing methods typically adopt the sequence-to-
item (seq2item) training strategy, which supervises a sequence model with a user's next …

Learning user representations based on historical behaviors lies at the core of modern
recommender systems. Recent advances in sequential recommenders have convincingly …

In session-based or sequential recommendation, it is important to consider a number of
factors like long-term user engagement, multiple types of user-item interactions such as …

Recent methods in sequential recommendation focus on learning an overall embedding
vector from a user's behavior sequence for the next-item recommendation. However, from …

Building effective recommender systems for domains like fashion is challenging due to the
high level of subjectivity and the semantic complexity of the features involved (ie, fashion …

In the field of sequential recommendation, deep learning--(DL) based methods have
received a lot of attention in the past few years and surpassed traditional models such as …

Personalized rating prediction is an important research problem in recommender systems.
Although the latent factor model (eg, matrix factorization) achieves good accuracy in rating …