Browse: U

Recommender systems typically rely on implicit feedback (e.g., clicks) to infer user preferences. However, such data is inherently prone to various biases, including position bias and popularity bias.

Despite growing capabilities, LLMs remain susceptible to prompt injections: malicious inputs that can manipulate or hijack models. In particular, it has recently been observed that LLMs can exhibit ro

Market indices track the performance of a group of stocks and serve as benchmarks for the overall market. This module covers how major indices like the S&P 500, Dow Jones, and NASDAQ are constructed, weighted, and used by investors.

A beginner-friendly overview of the global AML regulatory framework: FATF recommendations, the Bank Secrecy Act, the EU Anti-Money Laundering Directives, and how they fit together.

Unger et al quantify global money laundering at 2-5% of GDP and analyze its macroeconomic effects on growth, interest rates, exchange rates, and income distribution.

Industrial recommenders rely on two model families that have evolved largely independently: feature-interaction models over multi-field user/item features, and sequential models over user-behavior his