Modern Recommender Architectures
Feature-interaction models, multi-task ranking, sequential and generative recommenders, and LLMs in the loop
Learn what changed inside the ranking and retrieval models of a recommender after about 2016 and defend each choice in an interview: feature-interaction models (Wide and Deep, DeepFM, DCN and DCN-v2 cross layers with the degree-of-interaction claim verified numerically, embedding tables, optimizer-state memory and hashing collisions with the closed-form collision rate checked by simulation), multi-task ranking (shared-bottom versus MMoE versus PLE, a hand-computed MMoE gating pass, task weighting, fusion formulas and a measured cliff when one weight moves by 2.5x, and an honest null result for MMoE on a toy), user sequence modeling (GRU4Rec, SASRec, BERT4Rec, target attention in DIN and DIEN, SIM and ETA for 10,000-event histories, cost arithmetic for each), generative recommenders (action-as-token formulation in the style of HSTU, semantic IDs from RQ-VAE and generative retrieval in the style of TIGER, tradeoffs against two-tower plus ANN, all labeled as not measured here), LLMs in recommender systems (features, reranking, conversation, cost, where they do not help), retrieval improvements (multi-interest, mixed and cross-batch negatives, the logQ correction measured in a retrieval experiment where recall@10 moves from 0.019 to 0.214), debiasing and feedback loops (exposure and popularity bias, inverse propensity weighting in training, diversity and calibration re-ranking), training systems (streaming training, feature freshness, embedding staleness, sharded tables), evaluation (recall@k, NDCG, AUC versus GAUC measured on users with different base rates, sampled metrics, counterfactual replay, interleaving, A/B tests, long-term holdouts and a time-leakage experiment), and a worked example of a short-video ranker.
Practice questions (6)
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Design a Multi-Task Short-Video Ranker and Its Fusion Formula
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Choose a Sequence Model for 10,000-Event User Histories
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Argue For and Against Generative Retrieval Versus Two-Tower Plus ANN
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Diagnose and Fix Popularity Bias in a Retrieval Model
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Estimate Embedding Memory, Optimizer State and Hashing Collisions
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