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Wantedly RecSys 2020 参加レポート④ - 推薦システムにおける「Similarity / 類似性」とは ~ Netflix における事例 ~ | Wantedly Engineer Blog
こんにちは、ウォンテッドリーでデータサイエンティスト及びデータを活用したプロダクトのマネージャーを務めている松村です。2020年9月22日から9月26日にかけてオンラインで開催された RecSys 2020 に参加してきました。 こんにちは、ウォンテッドリーでデータサイエンティスト及びデータを活用したプロダクトのマネージャーを務めている松村です。2020年9月22日から9月26日にかけてオンラインで開催された RecSys 2020 に当社のデータサイエンティスト及び機械学習エンジニア５名で聴講参加及び、そのうちの３名が併設の RecSys Challenge の Workshop において口頭発表を行いました。その参加報告を行いたいと思います。 本記事では概要報告に留まりますが、明日からは参加メンバーが実際に発表を聴講して気にな せっかくなので参加メンバーで面白かったセッションを紹介する
GitHub - raghavan/PdfGptIndexer: An efficient tool for indexing and searching PDF text data using OpenAI API and FAISS (Facebook AI Similarity Search) index, designed for rapid information retrieval and superior search accuracy.
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用語「コサイン類似度」について説明。2つのベクトルが「どのくらい似ているか」という類似性を表す尺度で、具体的には2つのベクトルがなす角のコサイン値のこと。1なら「似ている」を、-1なら「似ていない」を意味する。主に文書同士の類似性を評価するために使われている。 連載目次 用語解説 数学／統計学／機械学習におけるコサイン類似度（Cosine Similarity）とは、2つのベクトルが「どのくらい似ているか」という類似性を表す尺度で、具体的には（ベクトル空間における）2つのベクトルがなす角のコサイン値のことである。この値は、2つのベクトルの内積（＝向きと大きさを持つベクトル同士の掛け算）を、2つのベクトルの大きさ（＝L2ノルム）で割ることで計算される。 この計算によって値が-1～1の範囲に正規化されるので、コサイン類似度が、 1なら「0度で、同じ向きのベクトル＝完全に似ている」 0なら「90
GitHub - pgvector/pgvector: Open-source vector similarity search for Postgres
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GitHub - upstash/semantic-cache: A fuzzy key value store based on semantic similarity rather lexical equality.
Semantic Cache is a tool for caching natural text based on semantic similarity. It's ideal for any task that involves querying or retrieving information based on meaning, such as natural language classification or caching AI responses. Two pieces of text can be similar but not identical (e.g., "great places to check out in Spain" vs. "best places to visit in Spain"). Traditional caching doesn't re
Vector similarity search is a game-changer in the world of search. It allows us to efficiently search a huge range of media, from GIFs to articles — with incredible accuracy in sub-second timescales for billion+ size datasets. One of the key components to efficient search is flexibility. And for that we have a wide range of search indexes available to us — there is no ‘one-size-fits-all’ in simila
Introduction to Facebook AI Similarity Search (Faiss) | Pinecone
Fortunately, it’s a brilliantly simple process to get started with. And in this article, we’ll explore some of the options FAISS provides, how they work, and — most importantly — how Faiss can make our search faster. Check out the video walkthrough here: What is Faiss?Before we get started with any code, many of you will be asking — what is Faiss? Faiss is a library — developed by Facebook AI — th
Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more
Text similarity search with vector fields — Elastic Search Labs
From its beginnings as a recipe search engine, Elasticsearch was designed to provide fast and powerful full-text search. Given these roots, improving text search has been an important motivation for our ongoing work with vectors. In Elasticsearch 7.0, we introduced experimental field types for high-dimensional vectors, and now the 7.3 release brings support for using these vectors in document scor
eBay’s Blazingly Fast Billion-Scale Vector Similarity Engine
The Similarity Engine's use cases include item-to-item similarity for text and image modality and user-to-item personalized recommendations based on a user’s historical behavior data. Introduction Often, ecommerce marketplaces provide buyers with listings similar to those previously visited by the buyer, as well as a personalized shopping experience based on profiles, past shopping histories and b
Uncanny similarity of unique inserts in the 2019-nCoV spike protein to HIV-1 gp120 and Gag
New Results Uncanny similarity of unique inserts in the 2019-nCoV spike protein to HIV-1 gp120 and Gag Prashant Pradhan, Ashutosh Kumar Pandey, Akhilesh Mishra, Parul Gupta, Praveen Kumar Tripathi, Manoj Balakrishnan Menon, James Gomes, Perumal Vivekanandan, Bishwajit Kundu
How to calculate cosine similarity of array in BigQuery - ITips
How can we calculate cosine similarity in BigQuery ? BigQuery(BQ) is very useful for data analysis or processing. It is good at handling huge data. It returns summary result in short time. BQ has one useful data format array. When we consider array as vector, we may want cosine similarity of vectors. So how can we get cosine similarity ? So today I introduce about "How to calculate cosine similari
GitHub - tensorflow/similarity: TensorFlow Similarity is a python package focused on making similarity learning quick and easy.
Tensorflow Similarity offers state-of-the-art algorithms for metric learning along with all the necessary components to research, train, evaluate, and serve similarity and contrastive based models. These components include models, losses, metrics, samplers, visualizers, and indexing subsystems to make this quick and easy. With Tensorflow Similarity you can train two main types of models: Self-supe
Big Little Lions: Disney’s New ‘Lion King’ Dodges the ‘Kimba’ Similarity Issue
It’s hard to overstate how popular the works of Tezuka — who died in 1989, around the same time Disney started work on The Lion King — were in Japan during his lifetime. In her 1997 essay “Japanese Culture and Popular Consciousness: Disney’s The Lion King vs. Tezuka’s Jungle Emperor,” Yasue Kuwahara, a professor at Northern Kentucky University and director of popular culture studies, says Tezuka “
How to do text similarity search and document clustering in BigQuery
BigQuery offers the ability to load a TensorFlow SavedModel and carry out predictions. This capability is a great way to add text-based similarity and clustering on top of your data warehouse. Follow along by copy-pasting queries from my notebook in GitHub. You can try out the queries in the BigQuery console or in an AI Platform Jupyter notebook. Text embeddings are useful for document similarity
Ultimate Guide To Text Similarity With Python - NewsCatcher
Learn the different similarity measures and text embedding techniques. Play around with code examples and develop a general intuition. In this article, you will learn about different similarity metrics and text embedding techniques. By the end, you'll have a good grasp of when to use what metrics and embedding techniques. You’ll also get to play around with them to help establish a general intuiti
cosine similarityの重み付けを使った、パーソナライズできるrecommender systemのベースライン - メモ帳
アイテム情報とユーザー情報を組み合わせた、パーソナライズされた推薦を行う基本的なシステムを紹介します。重み付けしたcosine similarity (コサイン類似度)によるシンプルな手法です。いわゆるcontent-basedなrecommendになっています。 機械学習を使った推薦システムでは、metric learningやautoencoderなどで高尚な特徴量に変換し、類似度の大きさを指標としたものが派手な話題性の高い手法だと思います。しかし、これらの手法は直感的でないので、うまく学習できないと全く使えないものになると思います。そこで、保守的な、ベースラインとして使う想定の手法を紹介したいと思います。 推薦システム まずは、recommendの概要を簡単にまとめます。 recommnedは主に以下の2種類のアルゴリズムが使われます。 content-based (コンテンツベース
Uncanny similarity of unique inserts in the 2019-nCoV spike protein to HIV-1 gp120 and Gag
Uncanny similarity of unique inserts in the 2019-nCoV spike protein to HIV-1 gp120 and Gag Prashant Pradhan$1,2 , Ashutosh Kumar Pandey$1 , Akhilesh Mishra$1 , Parul Gupta1 , Praveen Kumar Tripathi1 , Manoj Balakrishnan Menon1 , James Gomes1 , Perumal Vivekanandan*1 and Bishwajit Kundu*1 1 Kusuma School of biological sciences, Indian institute of technology, New Delhi-110016, India. 2 Acharya Nare
Cosine-similarity is the cosine of the angle between two vectors, or equivalently the dot product between their normalizations. A popular application is to quantify semantic similarity between high-dimensional objects by applying cosine-similarity to a learned low-dimensional feature embedding. This can work better but sometimes also worse than the unnormalized dot-product between embedded vectors
Cross Similarityとは 動的時間伸縮法(DTW)の派生で、部分一致に特化したパターン抽出方法。 DTWは時系列データ同士の距離マトリクスを作り、1本の最小経路を見つけそれを距離とするメトリクスであるが、Cross Similarityは部分マッチングのため、経路を複数見つけてくれる。 DTWについてのわかりやすい説明はこちら Cross Similarityについては↓の論文を参照 Discovery of Cross Similarity in Data Science(Toyoda et al. 2010) なぜ、Cross Similarityか 鳥の鳴き声の長時間データから、鳴き声のみをセグメンテーションしたいとき、鳴き声の波形は類型を繰り返すという特徴があり、この手法に合うのではないかと考えたため、実験。 環境 python 3.7.4 librosa 0.7.2
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コサイン類似度（Cosine Similarity）とは？
Nearest Neighbor Indexes for Similarity Search | Pinecone
Announcing ScaNN: Efficient Vector Similarity Search
類似性学習のためのTensorFlowライブラリ「TensorFlow Similarity」がリリース ~ 類似性モデルを簡単かつ迅速にトレーニング
Is Cosine-Similarity of Embeddings Really About Similarity?
【音声解析】LibrosaでCross Similarityを求める - Qiita
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