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TriMLP: A Foundational MLP-Like Architecture for Sequential Recommendation
In this work, we present TriMLP as a foundational MLP-like architecture for the sequential
recommendation, simultaneously achieving computational efficiency and promising …
recommendation, simultaneously achieving computational efficiency and promising …
Are we really achieving better beyond-accuracy performance in next basket recommendation?
Next basket recommendation (NBR) is a special type of sequential recommendation that is
increasingly receiving attention. So far, most NBR studies have focused on optimizing the …
increasingly receiving attention. So far, most NBR studies have focused on optimizing the …
Scaling session-based transformer recommendations using optimized negative sampling and loss functions
This work introduces TRON, a scalable session-based Transformer Recommender using
Optimized Negative-sampling. Motivated by the scalability and performance limitations of …
Optimized Negative-sampling. Motivated by the scalability and performance limitations of …
A reproducible analysis of sequential recommender systems
Sequential Recommender Systems (SRSs) have emerged as a highly efficient approach to
recommendation systems. By leveraging sequential data, SRSs can identify temporal …
recommendation systems. By leveraging sequential data, SRSs can identify temporal …
Who will purchase this item next? Reverse next period recommendation in grocery shop**
Recommender systems have become an essential instrument to connect people to the items
that they need. Online grocery shop** is one scenario where this is very clear. So-called …
that they need. Online grocery shop** is one scenario where this is very clear. So-called …
Right Tool, Right Job: Recommendation for Repeat and Exploration Consumption in Food Delivery
From e-commerce to music and news, recommender systems are tailored to specific
scenarios. While researching generic models applicable to various scenarios is crucial …
scenarios. While researching generic models applicable to various scenarios is crucial …
Balancing habit repetition and new activity exploration: A longitudinal micro-randomized trial in physical activity recommendations
As repetition of activities can establish habits and exploration of new ones can provide a
healthy variety, we investigate how a recommender system for physical activities can …
healthy variety, we investigate how a recommender system for physical activities can …
Recommender for Its Purpose: Repeat and Exploration in Food Delivery Recommendations
Recommender systems have been widely used for various scenarios, such as e-commerce,
news, and music, providing online contents to help and enrich users' daily life. Different …
news, and music, providing online contents to help and enrich users' daily life. Different …
TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics
Future link prediction is a fundamental challenge in various real-world dynamic systems. To
address this, numerous temporal graph neural networks (temporal GNNs) and benchmark …
address this, numerous temporal graph neural networks (temporal GNNs) and benchmark …
To copy, or not to copy; that is a critical issue of the output softmax layer in neural sequential recommenders
Recent studies suggest that the existing neural models have difficulty handling repeated
items in sequential recommendation tasks. However, our understanding of this difficulty is …
items in sequential recommendation tasks. However, our understanding of this difficulty is …