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Found 7 papers, 3 papers with code
RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm
For the model part, we adopt Generative Pre-training Transformer (GPT) as the sequential recommendation model and design a user modular to capture personalized information.
Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation
This causes two difficulties in designing effective algorithms: first, the majority of users only have a few interactions with the system and there is no enough data for learning; second, there are no negative samples in the implicit feedbacks and it is a common practice to perform negative sampling to generate negative samples.
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
\textbf{G}raph \textbf{C}onvolutional \textbf{N}etwork (\textbf{GCN}) is widely used in graph data learning tasks such as recommendation.
Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning
We predict users' preferences with the model and learn it by maximizing likelihood of observed data labels, i. e., a user prefers her positive samples and has no interests in her unvoted samples.
Spectrum-enhanced Pairwise Learning to Rank
However, there are some demerits of side information: (1) the extra data is not always available in all recommendation tasks; (2) it is only for items, there is seldom high-level feature describing users.
Visually-aware Recommendation with Aesthetic Features
While recent developments on visually-aware recommender systems have taken the product image into account, none of them has considered the aesthetic aspect.
Aesthetic-based Clothing Recommendation
Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner.
