user-behavior

[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"

Working white paper for OpenUBA

This repository analyzes Instagram user engagement using SQL queries to extract insights into user behavior, marketing effectiveness, and platform trends, aiming to optimize strategies, improve retention, and ensure platform integrity.

End-to-end streaming platform user behavior EDA with synthetic NOICE-style data, business insights, visuals, and Streamlit dashboard.

Network analysis to detect social communities, uncover behavioral patterns, and support data-driven content targeting.

Task of DLUT Big Data Department & SensorsData, third semester. Cooperate with @Bellick

Custom BI Tool (WP Plugin) – "Mixpanel Tracker" developed for Product Analysts, UX Researchers, and Business Owners to track user behavior, gain business insights, and optimize product performance.

A MATLAB-based interactive app that analyzes Disneyland attraction visit trends using geo-tagged Flickr data. Visualizes crowd flow, attraction popularity, and visit sequences across months and years using heatmaps, pie charts, bar graphs, and more.

SQL-based ecommerce performance analysis using Google BigQuery and the Google Analytics sample dataset, delivering insights on traffic, user behavior, revenue by channel, purchasing patterns, and ecommerce funnel conversion.

Behavioral analysis SDK that classifies browser sessions as human, bot, or LLM agent using client-side detection rules.

SQL exploratory data analysis (EDA) of Spotify streaming history to uncover user listening behavior and engagement insights

Social media analytics project

"Dataherd-Raika is a library designed to simulate large-scale user behavior datasets. It takes a single user event (like a click or keyword input) and, by applying simple probability distributions and custom variables, expands it into a vast dataset."

Event-driven feature flag API with deterministic rollouts, machine learning-assisted decisions, and safe fallback behavior.

Exploratory data analysis, A/B testing, and predictive modeling for a mobile game. The project includes KPI tracking (DAU, ARPU, ARPDAU, Retention, ROAS), evaluation of experiment results on engagement and monetization, and a machine learning approach to predict user purchase behavior within 30 days based on the first 7 days of activity.

Official Inspectlet JavaScript SDKs — session recording, heatmaps, form analytics, and AI session insights for React, Next.js, and any browser app.

生鲜电商用户行为分析 | SQL窗口函数 + RFM/Cohort建模 + 7张可视化图表

Deep learning-based analysis of mobile device usage and user behavior using ANN. Includes data cleaning, EDA, feature engineering, model training, and infographic visualization.

End-to-end e-commerce user behavior analysis | 电商用户行为全链路分析

Product analytics project focused on e-commerce conversion optimization and user funnel analysis.