A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation
Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …
understanding, research in recommendation has shifted to inventing new recommender …
A survey on session-based recommender systems
Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …
consumption, services, and decision-making in the overloaded information era and digitized …
Tabular data: Deep learning is not all you need
R Shwartz-Ziv, A Armon - Information Fusion, 2022 - Elsevier
A key element in solving real-life data science problems is selecting the types of models to
use. Tree ensemble models (such as XGBoost) are usually recommended for classification …
use. Tree ensemble models (such as XGBoost) are usually recommended for classification …
Revisiting deep learning models for tabular data
The existing literature on deep learning for tabular data proposes a wide range of novel
architectures and reports competitive results on various datasets. However, the proposed …
architectures and reports competitive results on various datasets. However, the proposed …
Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems
Learning effective feature crosses is the key behind building recommender systems.
However, the sparse and large feature space requires exhaustive search to identify effective …
However, the sparse and large feature space requires exhaustive search to identify effective …
Time interval aware self-attention for sequential recommendation
Sequential recommender systems seek to exploit the order of users' interactions, in order to
predict their next action based on the context of what they have done recently. Traditionally …
predict their next action based on the context of what they have done recently. Traditionally …
Neural collaborative filtering vs. matrix factorization revisited
Embedding based models have been the state of the art in collaborative filtering for over a
decade. Traditionally, the dot product or higher order equivalents have been used to …
decade. Traditionally, the dot product or higher order equivalents have been used to …
Transformers4rec: Bridging the gap between nlp and sequential/session-based recommendation
Much of the recent progress in sequential and session-based recommendation has been
driven by improvements in model architecture and pretraining techniques originating in the …
driven by improvements in model architecture and pretraining techniques originating in the …
Self-attentive sequential recommendation
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 …
to capture the'context'of users' activities on the basis of actions they have performed recently …
Autoint: Automatic feature interaction learning via self-attentive neural networks
Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking
on an ad or an item, is critical to many online applications such as online advertising and …
on an ad or an item, is critical to many online applications such as online advertising and …