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Found 8 papers, 4 papers with code
Poisoning Attacks against Recommender Systems: A Survey
This survey paper provides a systematic and up-to-date review of the research landscape on Poisoning Attacks against Recommendation (PAR).
Poisoning Attacks Against Contrastive Recommender Systems
Contrastive learning (CL) has recently gained significant popularity in the field of recommendation.
Efficient Bi-Level Optimization for Recommendation Denoising
To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time.
Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation
To explore recommendation-specific auxiliary tasks, we first quantitatively analyze the heterogeneous interaction data and find a strong positive correlation between the interactions and the number of user-item paths induced by meta-paths.
Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion
By providing users with opportunities to experience goods without charge, a free trial makes adopters know more about products and thus encourages their willingness to buy.
Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack
To explore the robustness of recommender systems, researchers have proposed various shilling attack models and analyzed their adverse effects.
Path-Based Reasoning over Heterogeneous Networks for Recommendation via Bidirectional Modeling
Despite their effectiveness, these models are often confronted with the following limitations: (1) Most prior path-based reasoning models only consider the influence of the predecessors on the subsequent nodes when modeling the sequences, and ignore the reciprocity between the nodes in a path; (2) The weights of nodes in the same path instance are usually assumed to be constant, whereas varied weights of nodes can bring more flexibility and lead to expressive modeling; (3) User-item interactions are noisy, but they are often indiscriminately exploited.
Face Aging With Identity-Preserved Conditional Generative Adversarial Networks
By grouping faces with target age together, the objective of face aging is equivalent to transferring aging patterns of faces within the target age group to the face whose aged face is to be synthesized.
