offline evaluation

JD Retail Technology

Jul 1, 2024 · Artificial Intelligence

Generative Recommendation Systems for JD Alliance Advertising: Design, Implementation, and Evaluation

This article surveys how large language models reshape recommendation systems, details a generative recommender framework for JD Alliance ads—including item representation, model input, training, and inference—presents extensive offline and online experiments, and discusses future optimization directions.

JD AllianceLLMgenerative recommendation

0 likes · 25 min read

Generative Recommendation Systems for JD Alliance Advertising: Design, Implementation, and Evaluation

JD Tech Talk

Jun 13, 2024 · Artificial Intelligence

Generative Recommender Systems for JD Affiliate Advertising: Architecture, Methods, and Experimental Evaluation

This article surveys how large language models can reshape recommendation systems, describes the four-stage generative pipeline, details item representation techniques such as semantic IDs, presents a JD affiliate advertising use case with offline and online experiments, and outlines future optimization directions.

LLMcold startgenerative recommender

0 likes · 25 min read

Generative Recommender Systems for JD Affiliate Advertising: Architecture, Methods, and Experimental Evaluation

DataFunTalk

Mar 30, 2024 · Artificial Intelligence

Reinforcement Learning and Multi‑Task Recommendation: Two‑Stage Constrained Actor‑Critic and Multi‑Task RL Approaches at Kuaishou

This talk presents Kuaishou's research on combining reinforcement learning with multi‑task recommendation, detailing a two‑stage constrained actor‑critic method for short‑video ranking, a multi‑task RL framework, experimental results on offline and online systems, and practical Q&A insights.

KuaishouReinforcement Learningactor-critic

0 likes · 18 min read

Reinforcement Learning and Multi‑Task Recommendation: Two‑Stage Constrained Actor‑Critic and Multi‑Task RL Approaches at Kuaishou

DataFunSummit

Mar 9, 2024 · Artificial Intelligence

OPPO Advertising Recall Algorithm: Architecture, Model Selection, Offline Evaluation, Sample Optimization, and Future Directions

This article presents OPPO's comprehensive advertising recall system, detailing the transition from the old to the new architecture with ANN support, the selection of main‑road recall models, the construction of offline evaluation metrics, sample optimization techniques, model enhancements, multi‑scenario training strategies, and outlook for future improvements.

Sample Optimizationadvertisingdual-tower model

0 likes · 24 min read

OPPO Advertising Recall Algorithm: Architecture, Model Selection, Offline Evaluation, Sample Optimization, and Future Directions

Sohu Tech Products

Jan 3, 2024 · Artificial Intelligence

OPPO Advertising Recall Algorithm: Architecture, Model Selection, Evaluation, and Optimization

OPPO revamped its advertising recall system by replacing a latency‑prone directional pipeline with an ANN‑based full‑ad personalized architecture, employing a dual‑tower LTR model, multi‑path auxiliary branches, refined offline metrics, price‑sensitive and hard‑negative sampling, and hybrid joint training, which together boosted ARPU by about 15%.

advertisinglarge-scale classificationmachine learning

0 likes · 24 min read

OPPO Advertising Recall Algorithm: Architecture, Model Selection, Evaluation, and Optimization

DataFunTalk

Dec 12, 2023 · Artificial Intelligence

Challenges and Considerations of Recommendation Systems: Evaluation, Data Leakage, and the Role of Large Models

This article examines recommendation system problem definitions, differences between academia and industry, offline evaluation pitfalls and data leakage issues, data construction challenges with datasets like MovieLens, and evaluates whether large language models can serve as effective solutions for modern recommendation tasks.

data leakagelarge language modelsmachine learning

0 likes · 20 min read

Challenges and Considerations of Recommendation Systems: Evaluation, Data Leakage, and the Role of Large Models

DataFunSummit

Oct 23, 2023 · Artificial Intelligence

Large Models in Recommendation Systems: Evaluation Challenges, Data Leakage, and Practical Considerations

This article examines how large language models fit into recommendation systems by discussing problem definitions, offline evaluation pitfalls such as data leakage, dataset construction issues exemplified by MovieLens, and the practical limits of using LLMs as a universal solution.

MovieLensdata leakagelarge language models

0 likes · 18 min read

Large Models in Recommendation Systems: Evaluation Challenges, Data Leakage, and Practical Considerations

Kuaishou Tech

Apr 27, 2023 · Artificial Intelligence

Two-Stage Constrained Actor‑Critic (TSCAC) for Short‑Video Recommendation

The paper models short‑video recommendation as a constrained Markov decision process and introduces a two‑stage constrained actor‑critic algorithm that jointly maximizes watch time while satisfying multiple interaction constraints, demonstrating superior offline and online performance on the KuaiRand dataset and Kuaishou app.

actor-criticconstrained optimizationoffline evaluation

0 likes · 7 min read

Two-Stage Constrained Actor‑Critic (TSCAC) for Short‑Video Recommendation

Qunar Tech Salon

Aug 21, 2016 · Artificial Intelligence

Hotel Search Ranking: Problem Definition, Model Construction, Feature Engineering, and Offline Evaluation

This article presents a comprehensive overview of hotel search ranking, covering problem definition, the distinction between ranking and probability estimation, handling position bias, detailed feature engineering, the AnyBoost linear boosting model, offline evaluation methods, and observed online performance improvements.

feature engineeringhotel rankinglearning to rank

0 likes · 7 min read