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Found 7 papers, 5 papers with code
Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation
To this end, we first address the characteristics of multi-behavior sequences that should be considered in SRSs, and then propose novel methods for Dynamic Multi-behavior Sequence modeling named DyMuS, which is a light version, and DyMuS+, which is an improved version, considering the characteristics.
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability
We first formulate a task that requires to predict which items each user will consume in the recent period of the training time based on users' consumption history.
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Sequential Recommendation
on Amazon Cell Phones
Unsupervised Proxy Selection for Session-based Recommender Systems
Session-based Recommender Systems (SRSs) have been actively developed to recommend the next item of an anonymous short item sequence (i. e., session).
Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation
Existing studies regard the time information as a single type of feature and focus on how to associate it with user preferences on items.
Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain
We release large-scale datasets of users{'} comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain.
Interest Sustainability-Aware Recommender System
More precisely, we first predict the interest sustainability of each item, that is, how likely each item will be consumed in the future.
Sparse Network Inversion for Key Instance Detection in Multiple Instance Learning
Since a positive bag contains both positive and negative instances, it is often required to detect positive instances (key instances) when a set of instances is categorized as a positive bag.
