Similarity Detection

Architect

Oct 18, 2021 · Fundamentals

Understanding Simhash: From Traditional Hash to Random Projection and LSH

This article explains the principles behind Simhash, covering the shortcomings of traditional hash functions, the use of cosine similarity, random projection for dimensionality reduction, locality‑sensitive hashing, random hyperplane hashing, implementation steps, query optimization with the pigeonhole principle, and the algorithm's limitations in short‑text scenarios.

Locality Sensitive HashingRandom ProjectionSimhash

0 likes · 18 min read

Understanding Simhash: From Traditional Hash to Random Projection and LSH

Xianyu Technology

Sep 2, 2021 · Artificial Intelligence

Real-time Product Matching and User Profiling System for Personalized Item Selection

The paper introduces a product‑matching and user‑profiling system that builds themed collections by comparing new items to cold‑start samples using a two‑stage similarity pipeline—exact edit‑distance and pHash checks followed by doc2vec and OCR‑based embeddings—and then profiles sellers with RFM and clustering to highlight attributes like recent C2C sales volume, achieving about 80 % precision in a license‑plate bidding scenario while outlining future fusion improvements.

Machine LearningProduct SelectionSimilarity Detection

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Real-time Product Matching and User Profiling System for Personalized Item Selection

37 Interactive Technology Team

Dec 13, 2018 · Artificial Intelligence

A 2‑Channel CNN Method for Automatic Game Asset Tag Generation and Similarity Recommendation

The paper introduces an improved two‑channel CNN, built on a shared VGG16 backbone and a hinge‑loss metric, to automatically generate numeric tags for game advertising assets by learning content and style similarity, achieving over 97% test accuracy and enabling efficient ad placement and asset management.

2-channel networkCNNDeep Learning

0 likes · 14 min read

A 2‑Channel CNN Method for Automatic Game Asset Tag Generation and Similarity Recommendation