Multi-scenario and multi-task aware feature interaction for recommendation system
Multi-scenario and multi-task recommendation can use various feedback behaviors of users
in different scenarios to learn users' preferences and then make recommendations, which …
in different scenarios to learn users' preferences and then make recommendations, which …
Disentangled-feature and composite-prior VAE on social recommendation for new users
Social recommendation has been an effective approach to solve the new user
recommendation problem based on user-item interactions and user-user social relations …
recommendation problem based on user-item interactions and user-user social relations …
Quantifying predictability of sequential recommendation via logical constraints
The sequential recommendation is a compelling technology for predicting users' next
interaction via their historical behaviors. Prior studies have proposed various methods to …
interaction via their historical behaviors. Prior studies have proposed various methods to …
Interpretable click-through rate prediction through distillation of the neural additive factorization model
An accurate estimation of the click-through rate (CTR), that is, the probability of clicking on a
recommended advertisement item online, is crucial for advertising agencies to make …
recommended advertisement item online, is crucial for advertising agencies to make …
Upper bound on the predictability of rating prediction in recommender systems
The task of rating prediction has undergone extensive scrutiny, employing diverse modeling
approaches to enhance accuracy. However, it remains uncertain whether a maximum …
approaches to enhance accuracy. However, it remains uncertain whether a maximum …
Modeling within-basket auxiliary item recommendation with matchability and ubiquity
Within-basket recommendation is to recommend suitable items for the current basket with
some already known items. The within-basket auxiliary item recommendation (WBAIR) is to …
some already known items. The within-basket auxiliary item recommendation (WBAIR) is to …
Limits of predictability in top-N recommendation
Top-N recommendation systems aim to recommend a small group of N items to users from
many products, and the accuracy of the system is a commonly used metric to evaluate its …
many products, and the accuracy of the system is a commonly used metric to evaluate its …
Multi-view improved sequence behavior with adaptive multi-task learning in ranking
Y Wang, D Zhang, A Wulamu - Applied Intelligence, 2023 - Springer
Click through rate (CTR) and Conversion Rate (CVR) are core tasks in e-commerce
recommender systems. Sequence behavior and multi-task learning have been widely used …
recommender systems. Sequence behavior and multi-task learning have been widely used …
Multi-head self-attention recommendation model based on feature interaction enhancement
In the recommendation system, click-through rate (CTR) prediction is a popular research
direction. Aiming at the problem of excessive compression of features in Factorization …
direction. Aiming at the problem of excessive compression of features in Factorization …
Re-sort: Removing spurious correlation in multilevel interaction for ctr prediction
Click-through rate (CTR) prediction is a critical task in recommendation systems, serving as
the ultimate filtering step to sort items for a user. Most recent cutting-edge methods primarily …
the ultimate filtering step to sort items for a user. Most recent cutting-edge methods primarily …