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பைடு நூலகம்
Matrix factorization
LFM(Latent factor model)
Matrix factorization
Probabilistic MF
Salakhutdinov, Ruslan, and Andriy Mnih. "Probabilistic matrix factorization."NIPS. Vol. 20. 2011.
collaborative filtering
The Long Tail
User-based CF
Konstan, Joseph A., et al. "GroupLens: applying collaborative filtering to Usenet news." Communications of the ACM 40.3 (1997): 77-87
Probabilistic MF
Probabilistic MF
Automatic Complexity Control
Constrained PMF
Experimental Results
Experimental Results
Problem
评价一个推荐系统标准
算法效率、可解释性
简单的算法+海量数据应该是能符合实际生产环境
Thanks !
Cosine Similarity
Jaccard Coefficient
Neighborhoods
K-neighborhoods or Threshold-based neighborhoods
Similarity Computing
Similarity Computing
Item-based CF
User-based CF
Grouplens
—The rating servers predict scores based on the heuristic that people who agreed in the past will probably agree again.
—Basic idea: recommend items similar to users favorite items. —GroupLens Architecture Overview —A Dynamic and Fast-Paced Information System —Ratings Sparsity —Performance Challenges
Content-based
example (movie)
Item Profiles - its genre - the participating actors - Its box office popularity - so forth User Profiles - movies and score list
Matrix factorization
Yehuda, Robert Bell, and Chris Volinsky. "Matrix factorization techniques for recommender systems." Computer 42.8 (2009): 30-37.
Matrix factorization
A basic matrix factorization model
Matrix factorization
ˆ R P QT = R
Matrix factorization
K ˆij piT q j k r 1 pik qkj
2 K 2 ˆij ) 2 (rij k eij (rij r p q ) 1 ik kj
history
Content Filtering [Before 1992] Grouplens [1994] —Frist recommender system using rating data Movielens[1997] —Frist movie recommender system —Provide well-known dataset for researchers Amazon —proposed item-based collaborative filtering (Patent is filed in 1998 and issued in 2001) Netflix Prize[2006] —Latent Factor Model(SVD,RSVD,NSVD,SVD++) —Yehuda Koren’s team get prize
Recommender Systems Introduction
Cheng Shi
20.Dec.2016
Outline
Backgrounds
& history
Algorithms
Content-based Recommendation Collaborative Filtering-based Recommendation • User-based Recommendation • Item-based Recommendation • Model-based Recommendation Problem
Backgrounds
Information overload is one of the most critical problems, and personalized recommendation system is a powerful tool to solve this problem.
Matrix factorization
Matrix factorization
Biases：
Temporal dynamics： Confidence levels：
Browsing Additional input sources ： history,gender,age group
Zip code,income level
Linden, Greg, Brent Smith, and Jeremy York. "Amazon. com recommendations: Item-to-item collaborative filtering." IEEE Internet computing 7.1 (2003): 76-80.
User-based CF
Establishment of user model
Similarity Computing
Find similar users set
Euclidean Distance Similarity
Similarity Computing
Pearson Correlation Similarity
Item-based CF
Amazon.com
-Few details
-basic idea
-Scalability
Item-based CF
M users, N items
O( N M )
2
O( NM )
Item-based CF sample
Scalability
Amazon.com has more than 29 million customers and several million catalog items. For large retailers like Amazon.com, a good recommendation algorithm is scalable over very large customer bases and product catalogs, requires only subsecond processing time to generate online recommendations.
2006年，NETFLIX宣布，设立一项大赛，公 开征集电影推荐系统的最佳电脑算法，第一 个能把现有推荐系统的准确率提高10%的参赛 者将获得一百万美元的奖金。2009 年 9 月 21 日，来自全世界 186 个国家的四万多个 参赛团队经过近三年的较量，终于有了结果。 一个由工程师和统计学家组成的七人团队夺 得了大奖，拿到了那张百万美元的超大支票。
A Comparison
Scalability Diversity&
Precision
Netflix Prize
Netflix是一家美国在线视频网站，公司一 开始的主要业务是提供DVD和Blu-ray光盘的出 租服务。现在的主要业务是原创内容的网络流 媒体服务。2013年凭借高端自制美剧《纸牌屋》 和随后的多部剧集的超高质量引起全球瞩目。
history
After 2006 —Interpretability of the recommendation results —Join the sentiment analysis to the Matrix Factorization
—Recommender Systems with Deep Learning
Matrix factorization
LFM(Latent factor model)
Matrix factorization
Probabilistic MF
Salakhutdinov, Ruslan, and Andriy Mnih. "Probabilistic matrix factorization."NIPS. Vol. 20. 2011.
collaborative filtering
The Long Tail
User-based CF
Konstan, Joseph A., et al. "GroupLens: applying collaborative filtering to Usenet news." Communications of the ACM 40.3 (1997): 77-87
Probabilistic MF
Probabilistic MF
Automatic Complexity Control
Constrained PMF
Experimental Results
Experimental Results
Problem
评价一个推荐系统标准
算法效率、可解释性
简单的算法+海量数据应该是能符合实际生产环境
Thanks !
Cosine Similarity
Jaccard Coefficient
Neighborhoods
K-neighborhoods or Threshold-based neighborhoods
Similarity Computing
Similarity Computing
Item-based CF
User-based CF
Grouplens
—The rating servers predict scores based on the heuristic that people who agreed in the past will probably agree again.
—Basic idea: recommend items similar to users favorite items. —GroupLens Architecture Overview —A Dynamic and Fast-Paced Information System —Ratings Sparsity —Performance Challenges
Content-based
example (movie)
Item Profiles - its genre - the participating actors - Its box office popularity - so forth User Profiles - movies and score list
Matrix factorization
Yehuda, Robert Bell, and Chris Volinsky. "Matrix factorization techniques for recommender systems." Computer 42.8 (2009): 30-37.
Matrix factorization
A basic matrix factorization model
Matrix factorization
ˆ R P QT = R
Matrix factorization
K ˆij piT q j k r 1 pik qkj
2 K 2 ˆij ) 2 (rij k eij (rij r p q ) 1 ik kj
history
Content Filtering [Before 1992] Grouplens [1994] —Frist recommender system using rating data Movielens[1997] —Frist movie recommender system —Provide well-known dataset for researchers Amazon —proposed item-based collaborative filtering (Patent is filed in 1998 and issued in 2001) Netflix Prize[2006] —Latent Factor Model(SVD,RSVD,NSVD,SVD++) —Yehuda Koren’s team get prize
Recommender Systems Introduction
Cheng Shi
20.Dec.2016
Outline
Backgrounds
& history
Algorithms
Content-based Recommendation Collaborative Filtering-based Recommendation • User-based Recommendation • Item-based Recommendation • Model-based Recommendation Problem
Backgrounds
Information overload is one of the most critical problems, and personalized recommendation system is a powerful tool to solve this problem.
Matrix factorization
Matrix factorization
Biases：
Temporal dynamics： Confidence levels：
Browsing Additional input sources ： history,gender,age group
Zip code,income level
Linden, Greg, Brent Smith, and Jeremy York. "Amazon. com recommendations: Item-to-item collaborative filtering." IEEE Internet computing 7.1 (2003): 76-80.
User-based CF
Establishment of user model
Similarity Computing
Find similar users set
Euclidean Distance Similarity
Similarity Computing
Pearson Correlation Similarity
Item-based CF
Amazon.com
-Few details
-basic idea
-Scalability
Item-based CF
M users, N items
O( N M )
2
O( NM )
Item-based CF sample
Scalability
Amazon.com has more than 29 million customers and several million catalog items. For large retailers like Amazon.com, a good recommendation algorithm is scalable over very large customer bases and product catalogs, requires only subsecond processing time to generate online recommendations.
2006年，NETFLIX宣布，设立一项大赛，公 开征集电影推荐系统的最佳电脑算法，第一 个能把现有推荐系统的准确率提高10%的参赛 者将获得一百万美元的奖金。2009 年 9 月 21 日，来自全世界 186 个国家的四万多个 参赛团队经过近三年的较量，终于有了结果。 一个由工程师和统计学家组成的七人团队夺 得了大奖，拿到了那张百万美元的超大支票。
A Comparison
Scalability Diversity&
Precision
Netflix Prize
Netflix是一家美国在线视频网站，公司一 开始的主要业务是提供DVD和Blu-ray光盘的出 租服务。现在的主要业务是原创内容的网络流 媒体服务。2013年凭借高端自制美剧《纸牌屋》 和随后的多部剧集的超高质量引起全球瞩目。
history
After 2006 —Interpretability of the recommendation results —Join the sentiment analysis to the Matrix Factorization
—Recommender Systems with Deep Learning