Recsys2015で発表されたCriteo社の最新レコメンド情報を読む - Y's note
RecSys 2015: Large-scale real-time product recommendation at Criteo from Romain Lerallut www.slideshare.net criteo社のRecommendation Logic Criteo社の広告枠への配信に対するCTR/CVR予測モデルの話以外にもRecommendLogicに関する発表がRecsys2015であったので見てみました。一般的なRecommed Systemでは良く見受けられる構成や手法なのでインパクトがある内容ではありませんが共有します。Keywords : Logistic Regression, Hadoop, Memcached, collaboration filterling, Recsys2015, Criteo社のCTR/CVR予測モデルは下記論文などを参照 S
Interactive Recommender Systems with Netflix and Spotify
Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In the larger ecosystem of recommender systems used on a website, it is positioned between a lean-back recommendation experience and an active search for a specific piece of content. Besides this aspect, we will discuss several parts that are
http://nbviewer.ipython.org/github/balaca/twitter_graph/blob/master/twitter_graph.ipynb
This notebook was put together by Balachandar Arumugam. Source and license info is on GitHub. This notebook covers the following: 1. Connect to Twitter API, fetch friends' details and construct social-graph 2. Identify top-influencers by running Page Rank algorithm on Markov Transition matrix (from Social Graph) 3. Analyse adjacency-matrix and check for clusters of interest, which are discriminati
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
音楽聴き放題サービスAWAにおけるレコメンド手法の検討 (artist2vecの試み)