Download and preprocess popular sequential recommendation datasets
E-commerce recommendation system simulation using the Retailrocket dataset, Apriori algorithm, ASP.NET Core, FastAPI, DuckDB, and PostgreSQL.
Forecast short-horizon catalog attention on item graphs using CTMC + Wasserstein drift–diffusion (Retailrocket).
Client for recomendation service retailrocket.ru
基于Retailrocket的多阶段推荐系统：五路召回、RRF排序、PyTorch基线、FastAPI与Streamlit合成数据演示，强调防泄漏评估与诚实实验结论。
Production-ready Two-Stage Recommender System (iALS Retrieval + CatBoost Ranking) with FastAPI serving and Time-Split validation.
Product analysis of 2.76M real e-commerce events: session funnel, cohort retention, behavioral segments, and cart-recovery A/B test design.
Research-oriented e-commerce recommender system for session intent, candidate generation, funnel-aware ranking, cold start, catalog exposure, and serving tradeoffs.
End-to-end product analytics on 2.75M real e-commerce events: finding that returning visitors convert 5.3× better and validating a reactivation nudge with an A/B test. Python · SQL · SciPy · Power BI.
AI-powered Learning-to-Rank system for personalized quick-commerce product recommendations using LambdaMART, XGBoost Ranker, and SHAP explainability.
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