RecSys 2020: Highlights of a Special Conference


Read my take on the highlights of the 14th ACM Conference on Recommender Systems, such as the winners of the best long and short paper awards as well as an assortment of the best workshops and tutorials.

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

RecSys 2020: Highlights of a Special Conference2020-09-29T01:13:53+00:00

Multimodal Sequential Recommender Systems


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

Multimodal Sequential Recommender Systems2021-02-10T09:15:46+00:00

Multiplicative LSTM for sequence-based Recommenders


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

Multiplicative LSTM for sequence-based Recommenders2021-02-10T09:01:14+00:00