Research map / Probabilistic and scientific modeling / Molecules and proteins
Modeling Dynamic Missingness of Implicit Feedback for Recommendation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add inner/outer item constraints on Beta priors (H4MFc vs H4MF) (ablation) | H4MF · MovieLens-100K | HR@50: 0.3990 -> 0.4347 | Helped |
| add inner/outer item constraints on Beta priors (ablation) | H4MF · MovieLens-1M | HR@50: 0.2704 -> 0.3049 | Helped |
| add inner/outer item constraints on Beta priors (ablation) | H4MF · LastFM | HR@50: 0.1980 -> 0.2298 | Helped |
| model dynamic missingness with HMM user intents instead of static exposure (H4MF vs ExpoMF) | MF recommender · MovieLens-100K | HR@50: 0.3478 -> 0.3990 | Helped |
| dynamic missingness HMM (H4MF) vs WMF | MF recommender · LastFM | HR@50: 0.2144 -> 0.1980 | Hurt |
| increase number of user intents D beyond 2 (ablation) | H4MF and H4MFc · MovieLens-100K, MovieLens-1M, LastFM | HR@50 | Hurt |
| increase lambda_Outer above about 1 (ablation) | H4MFc · three datasets | HR@50 | Hurt |
| vary lambda_Inner (ablation) | H4MFc · three datasets | HR@50 | No clear effect |
| add item constraints to reduce redundancy between user intents (ablation) | H4MF · MovieLens-100K (D=2) | recommendation overlap: 80% -> 26% | Helped |
Closest papers by meaning
- Counterfactual Implicit Feedback Modeling (NeurIPS 2025 · similarity 0.89)
- Tripartite Collaborative Filtering with Observability and Selection for Debiasing Rating Estimation on Missing-Not-at-Random Data (AAAI 2021 · similarity 0.87)
- Deconvolving Feedback Loops in Recommender Systems (NeurIPS 2016 · similarity 0.87)
- Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning (ICML 2025 · similarity 0.86)
- Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation (NeurIPS 2024 · similarity 0.85)
- Influential Exemplar Replay for Incremental Learning in Recommender Systems (AAAI 2024 · similarity 0.84)
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