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Introduction to Recommender System. Guo , Guangming guogg.good@gmail.com. Outline . Background & Definition Some history worth noting Various applications Main-stream approach Evaluation Some resources. Outline . Background & Definition Related areas Challenges Paradigms

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introduction to recommender system

Introduction to Recommender System

Guo, Guangming

guogg.good@gmail.com

outline
Outline
  • Background & Definition
  • Some history worth noting
  • Various applications
  • Main-stream approach
  • Evaluation
  • Some resources

Lab of Semantic Computing and Data Mining

outline1
Outline
  • Background & Definition
    • Related areas
    • Challenges
    • Paradigms
  • Some history worth noting
  • Various applications
  • Main-stream approach
  • Evaluation
  • Some resources

Lab of Semantic Computing and Data Mining

become clear with basic concepts
Become clear with basic concepts
  • First step of learning
  • Building blocks of new ideas
  • Define the rules to play with
  • Prerequisites for communication

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definition of recommender systems
Definition of Recommender Systems
  • Also named recommendation systems
  • A subclass of information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item (such as music, books, or movies) or social element (e.g. people or groups) they had not yet considered, using a model built from the characteristics of an item (content-based approaches) or the user's social environment (collaborative filtering approaches). --http://en.wikipedia.org/wiki/Recommender

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more truth
More truth
  • Important vertical technique in data mining
  • One of the most success solution for industry
  • Became an independent research area in 1990s
    • Many highly reputed academic conferences such as SIGIR, KDD, ICML, WWW, EMNLP et al. have it as their subtopics.
    • RecSys is fully devoted to this area
  • Data mining/machine learning approach
    • 1) specifying heuristics that define the utility function and empirically validating its performance
    • 2) estimating the utility function that optimizes certain performance criterion, such as the mean square error.

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chanllenges
Chanllenges
  • Cold start
  • Long tail
  • Data sparsity
  • Scalability
  • Social & Temporal
  • Context-aware
  • Personality-aware
  • Being accuracy is not enough

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related research area
Related Research Area
  • Cognitive science
  • Text mining
  • Natural Language Processing
  • Information retrieval
  • Machine learning
  • Association mining
  • Approximation theory
  • Management science
  • Consumer choice in marketing

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paradigm of recsys
Paradigm of RecSys
  • Content-based recommendations:
    • recommended items similar to the ones the user preferred in the past;
  • Collaborative recommendations:
    • recommended items that people with similar tastes and preferences liked in the past;
  • Knowledge-based recommendations:
    • recommended items based existing knowledge models that fit the needs of users
  • Hybrid approaches:
    • Combination of various input data or/and composition various mechanism

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background
Background
  • Universe Problem in Information Age
    • Information overload
    • From SE to Recsys
    • pull vs. push
    • Web 1.0 vs. web 2.0
  • Leverage the existing user generated data
    • User profile
    • Behavior history on the web,Rating
    • Click through data, browse data
  • Great benefits(win-win)
    • Help users find valuable information
    • Help business make more profits

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outline2
Outline
  • Background & Definition
  • Some history worth noting
    • Netflix prize
  • Various applications
  • Main-stream approach
  • Evaluation
  • Some resources

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a peak in the history
A peak in the history
  • Research on collaborative filtering algorithm reached a peak during the Netflix movie recommendation competition
  • October 2, 2006 ~ September 21, 2009
  • RMSE
    • Must outperform baseline by 10%

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the million dollar programming prize
The Million Dollar Programming Prize
  • The Netflix Prize
    • Greatly energize the research in Recsys
    • Last from 2006 to 2009
  • Finalist: BellKor’sPragamatic Chaos team
    • A joint-team
    • Andreas Töscher and Michael Jahrer ( Commendo Research &Consulting GmbH), originally team BigChaos
    • Robert Bell, and Chris Volinsky (AT& T), Yehuda Koren (Yahoo),originally team BellKor
    • Martin Piotte and Martin Chabbert, originally team Pragmatic Theory
  • The ensemble Team
    • The most accurate algorithm in 2007 used an ensemble method of 107 different algorithmic approaches

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outline3
Outline
  • Background & Definition
  • Some history worth noting
  • Various applications
  • Main-stream approach
  • Evaluation
  • Some resources

Lab of Semantic Computing and Data Mining

existing applications
Existing applications
  • News/Article recommendation
  • Targeted Advertisement
  • Tags Recommendation
  • Mobile Recommendation
  • E-commerce
    • Books, movies, music…

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benefits
Benefits
  • Alternative to Search Engine
  • Boost the profit
    • Amazon et al.
  • Better user experience

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outline4
Outline
  • Background & Definition
  • Some history worth noting
  • Various applications
  • Main-stream approach
    • Content-based
    • Collaborative filtering
  • Evaluation
  • Some resources

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content based
Content-based
  • Simple compute the similarity
    • Cosine similarity or pearson correlation coefficient
    • TF-IDF
  • Utilize dimensionality reduction
    • LDA

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collaborative filtering
Collaborative filtering
  • Association mining
  • Memory-based
    • Nearest-neighbors
  • Model-based
    • Latent fator model
  • Some comparison
    • Space & time
    • Theory foundation and interpretability

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latent factor model
Latent factor model
  • LSI, pLSA, LDA, latent class model, Topic model et al.
  • A method based on matrix factorization/decomposition

where R is the rating matrix, P and Q are sub-matrix after dimension reduction

An low-rank approximation of the original matrix

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computations
Computations
  • Traditional SVD
    • Needs a simple method to complete the matrix
    • Cost on the completed dense matrix is very high
  • Situation changed in 2006 after the Netflix Prize
    • Simon Funk
    • Defined a cost function on the training data
  • To avoid overfitting, add regularization term
      • Gradient descent to optimize C(p,q)

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outline5
Outline
  • Background & Definition
  • Some history worth noting
  • Various applications
  • Main-stream approach
  • Evaluation
  • Some resources

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evaluation criterion
Evaluation Criterion
  • User satisfaction by quesionnaire
  • Precision
    • RMSE
    • Top-k
  • Coverage
  • Diversity
  • Novelty
  • Serendipity
    • Originally thinking recommendation has non-sense

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outline6
Outline
  • Background & Definition
  • Some history worth noting
  • Various applications
  • Main-stream approach
  • Evaluation
  • Some resources

Lab of Semantic Computing and Data Mining

slide25
葫芦项亮

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resources
Resources
  • www.recsyswiki.com
  • 各大推荐引擎资料汇总 by 大魁
    • http://blog.csdn.net/lzt1983/article/details/7914536

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