Multi-factor sequential re-ranking with perception-aware diversification
Feed recommendation systems, which recommend a sequence of items for users to browse
and interact with, have gained significant popularity in practical applications. In feed …
and interact with, have gained significant popularity in practical applications. In feed …
Multi-intention oriented contrastive learning for sequential recommendation
Sequential recommendation aims to capture users' dynamic preferences, in which data
sparsity is a key problem. Most contrastive learning models leverage data augmentation to …
sparsity is a key problem. Most contrastive learning models leverage data augmentation to …
Two-stage constrained actor-critic for short video recommendation
The wide popularity of short videos on social media poses new opportunities and
challenges to optimize recommender systems on the video-sharing platforms. Users …
challenges to optimize recommender systems on the video-sharing platforms. Users …
Reinforcing user retention in a billion scale short video recommender system
Recently, short video platforms have achieved rapid user growth by recommending
interesting content to users. The objective of the recommendation is to optimize user …
interesting content to users. The objective of the recommendation is to optimize user …
Contextual Distillation Model for Diversified Recommendation
The diversity of recommendation is equally crucial as accuracy in improving user
experience. Existing studies, eg, Determinantal Point Process (DPP) and Maximal Marginal …
experience. Existing studies, eg, Determinantal Point Process (DPP) and Maximal Marginal …
SMEF: Social-aware Multi-dimensional Edge Features-based Graph Representation Learning for Recommendation
Exploring user-item interaction cues is crucial for the performance of recommender systems.
Explicit investigation of interaction cues is made possible by using graph-based models …
Explicit investigation of interaction cues is made possible by using graph-based models …
Disentangled representation for diversified recommendations
Accuracy and diversity have long been considered to be two conflicting goals for
recommendations. We point out, however, that as the diversity is typically measured by …
recommendations. We point out, however, that as the diversity is typically measured by …
On Evaluation Metrics for Diversity-enhanced Recommendations
Diversity is increasingly recognized as a crucial factor in recommendation systems for
enhancing user satisfaction. However, existing studies on diversity-enhanced …
enhancing user satisfaction. However, existing studies on diversity-enhanced …
Diversifying Sequential Recommendation with Retrospective and Prospective Transformers
Previous studies on sequential recommendation (SR) have predominantly concentrated on
optimizing recommendation accuracy. However, there remains a significant gap in …
optimizing recommendation accuracy. However, there remains a significant gap in …
Cllp: Contrastive learning framework based on latent preferences for next poi recommendation
H Zhou, Z Jia, H Zhu, Z Zhang - … of the 47th International ACM SIGIR …, 2024 - dl.acm.org
Next Point-Of-Interest (POI) recommendation plays an important role in various location-
based services. Its main objective is to predict the users' next interested POI based on their …
based services. Its main objective is to predict the users' next interested POI based on their …