The 14th ACM Conference on Recommender Systems was special in many ways: a fully virtual conference that did an amazing job to keep social interaction alive – e
Sequential recommender systems are based on sequential user representations for a given user and sequence length. Each sequence consists of several items in temporal order. Sequential recommender systems aim at exploiting the temporal information that is hidden in the sequence of item interactions of the given user.
Since the invention of the internet, the availability and amount of information has increased steadily. Today we are facing problems of information overload and an ov
Traditional user-item recommenders often neglect the dimension of time, finding for each user a latent representation based on the user’s historical item interactions without any notion of recency and sequence of interactions. Sequence-based recommenders such as Multiplicative LSTMs tackle this issue.
Recommender Systems support the decision making processes of customers with personalized suggestions. They are widely used and influence the daily life of almost ever