#recsys
8 notes
- DeepFM DeepFM (Guo et al., 2017) is a CTR prediction model that combines an FM component and a Deep component in parallel, jointly learning low-order (explicit) and high-order (implicit) feature interactions.
- ESCM² (Entire Space Counterfactual Multi-Task Model) A model that integrates a counterfactual risk regularizer based on the Inverse Propensity Score (IPS) and the Doubly Robust estimator into ESMM, in order to address ESMM's two theoretical limitations — Inherent Estimation Bias (IEB) and Potential Independence Priority (PIP).
- ESMM (Entire Space Multi-Task Model) A multi-task model that addresses CVR's Sample Selection Bias and Data Sparsity problems simultaneously by exploiting the sequential user behavior $\text{impression} \to \text{click} \to \text{conversion}$ to learn CVR indirectly over the entire impression space.
- Factorization Machine The Factorization Machine (FM) is a general-purpose prediction model proposed by Rendle (2010) that models interactions between all pairs of features as inner products of latent factor vectors.
- LLM Multi-Layer Attribute Extraction for Cross-Domain Recommendation A case study on extracting a 3-layer attribute taxonomy (product / perceptual / theory-grounded) with LLM/VLM pipelines, turning it into user profiles and a mixture-of-experts adaptor, and plugging it into standard recommenders across two public domains (fashion + music).
- Multi-Task Learning A learning paradigm that jointly trains several related tasks, improving generalization through a shared representation.
- PNN PNN (Qu et al., 2016) is a CTR prediction model that introduces a product layer between the embedding layer and the DNN hidden layers, explicitly capturing the interactions among feature embeddings before passing them to the DNN.
- Wide and Deep Wide & Deep (Cheng et al., 2016) is a CTR prediction model that combines a linear wide component (memorization) with a DNN deep component (generalization). It was first deployed for Google Play app recommendation.