{"product_id":"154747","title":"Recommendation system ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYDPLHEuEsP95D5IAEMJmP8I2ekuQ.png?v=1765081665\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Recommendation system \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/105625468\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e It comprehensively covers recommender systems that provide personalized recommendations of products or services based on a user's previous searches or purchases.\u003cbr\u003e Recommender system methods have been applied to a variety of applications, including query log mining, social networking, news recommendations, and computer advertising. \u003cbr\u003eThis book is organized into three categories: Algorithms and Evaluation, Recommendations for Specific Domains and Contexts, and Advanced Topics and Applications.\u003cbr\u003e While primarily intended to serve as a textbook, it also introduces current topics and applications, making it useful for both industry professionals and researchers.\u003cbr\u003e Provides numerous examples and practice problems.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eChapter 1.\u003cbr\u003e Introduction to the Recommendation System\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 1.1 Overview\u003cbr\u003e 1.2 Goals of the Recommender System\u003cbr\u003e 1.21 Scope of Recommended Applications\u003cbr\u003e 1.3 Basic model of the recommendation system\u003cbr\u003e 1.3.1 Collaborative Filtering Model\u003cbr\u003e 1.3.1.1 Types of ratings\u003cbr\u003e 1.3.1.2 Relationship with missing value analysis\u003cbr\u003e 1.3.1.3 Collaborative Filtering as a Generalization of Classification and Regression Modeling\u003cbr\u003e 1.3.2 Content-based recommendation system\u003cbr\u003e 1.3.3 Knowledge-based recommendation system\u003cbr\u003e 1.3.31 Utility-based Recommender Systems\u003cbr\u003e 1.3.4 Demographic Recommendation System  \u003cbr\u003e1.3.5 Hybrid and Ensemble-Based Recommender Systems\u003cbr\u003e 1.3.6 Evaluation of the Recommender System\u003cbr\u003e 1.4 Domain-Specific Tasks of Recommender Systems\u003cbr\u003e 1.4.1 Context-based Recommendation System\u003cbr\u003e 1.4.2 Time-Sensitive Recommender Systems\u003cbr\u003e 1.4.3 Location-based recommendation system\u003cbr\u003e 1.4.4 Social Recommendation System\u003cbr\u003e 1.4.4.1 Structural Recommendations for Nodes and Links\u003cbr\u003e 1.4.4.2 Product and content recommendations that take social impact into account\u003cbr\u003e 1.4.4.3 Trustworthy Recommendation Systems\u003cbr\u003e 1.4.4.4 Recommendations using social tag feedback\u003cbr\u003e 1.5 Advanced Topics and Applications\u003cbr\u003e 1.5.1 Cold Start Problem in Recommender Systems\u003cbr\u003e 1.5.2 Attack-resistant recommendation system\u003cbr\u003e 1.5.3 Group Recommendation System\u003cbr\u003e 1.5.4 Multi-criteria recommender system\u003cbr\u003e 1.5.5 Active Learning in Recommender Systems\u003cbr\u003e 1.5.6 Privacy Protection in Recommendation Systems\u003cbr\u003e 1.5.7 Application Domain\u003cbr\u003e 1.6 Summary\u003cbr\u003e 1.7 References\u003cbr\u003e 1.8 Practice Problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 2.\u003cbr\u003e Neighbor-based collaborative filtering\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 2.1 Overview\u003cbr\u003e 2.2 Key Features of the Rating Matrix\u003cbr\u003e 2.3 Rating Prediction Using Neighborhood-Based Methodology  \u003cbr\u003e2.3.1 User-Based Neighborhood Model\u003cbr\u003e 2.3.1.1 Similarity function transformation\u003cbr\u003e 2.3.1.2 Variation of the prediction function\u003cbr\u003e 2.3.1.3 Variations of Peer Group Filtering\u003cbr\u003e 2.3.1.4 The Impact of the Long Tail\u003cbr\u003e 2.3.2 Item-based neighborhood model\u003cbr\u003e 2.3.3 Efficient implementation and computational complexity\u003cbr\u003e 2.3.4 Comparison of User-Based and Item-Based Methodologies\u003cbr\u003e 2.3.5 Advantages and Disadvantages of Neighborhood-Based Methodologies\u003cbr\u003e 2.3.6 An Integrated Perspective on User-Based and Item-Based Methodologies\u003cbr\u003e 2.4 Clustering and Neighborhood-Based Methodologies\u003cbr\u003e 2.5 Dimensionality Reduction and Neighborhood-Based Methodology\u003cbr\u003e 2.5.1 Handling Bias Issues\u003cbr\u003e 2.5.1.1 Maximum likelihood estimation\u003cbr\u003e 2.5.1.2 Direct matrix factorization of incomplete data\u003cbr\u003e 2.6 Regression Modeling Perspective of Neighbor Methodology\u003cbr\u003e 2.6.1 User-Based Nearest Neighbor Regression\u003cbr\u003e 2.6.1.1 Sparsity and Bias Problems\u003cbr\u003e 2.6.2 Item-based nearest neighbor regression\u003cbr\u003e 2.6.3 Combining User-Based and Item-Based Methods\u003cbr\u003e 2.6.4 Joint interpolation using similarity weights\u003cbr\u003e 2.6.5 Sparse Linear Model (SLIM)  \u003cbr\u003e2.7 Graph Models for Neighbor-Based Methods\u003cbr\u003e 2.7.1 User-Item Graph\u003cbr\u003e 2.7.1.1 Defining Neighborhood Using Random Walk\u003cbr\u003e 2.7.1.2 Defining neighbors using the Katz scale\u003cbr\u003e 2.7.2 User-User Graph\u003cbr\u003e 2.7.3 Item-Item Graph\u003cbr\u003e 2.8 Summary\u003cbr\u003e 2.9 References\u003cbr\u003e 2.10 Practice Problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 3.\u003cbr\u003e Model-based collaborative filtering\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 3.1 Overview\u003cbr\u003e 3.2 Decision and Regression Trees\u003cbr\u003e 3.2.1 Extending decision trees with collaborative filtering\u003cbr\u003e 3.3 Rule-based collaborative filtering\u003cbr\u003e 3.3.1 Leveraging Association Rules for Collaborative Filtering\u003cbr\u003e 3.3.2 Item-specific vs. user-specific models\u003cbr\u003e 3.4 Naive Bayes Collaborative Filtering\u003cbr\u003e 3.4.1 Overfitting Correction\u003cbr\u003e 3.4.2 Example of applying Bayes' method to binary ratings\u003cbr\u003e 3.5 Using an arbitrary classification model as a black box\u003cbr\u003e 3.5.1 Example: Using a Neural Network as a Black Box\u003cbr\u003e 3.6 Latent Factor Model\u003cbr\u003e 3.6.1 Geometric Intuition about Latent Factor Models\u003cbr\u003e 3.6.2 Low-dimensional intuition about latent factor models\u003cbr\u003e 3.6.3 Basic matrix factorization principles  \u003cbr\u003e3.6.4 Unconstrained matrix factorization\u003cbr\u003e 3.6.4.1 Stochastic gradient descent\u003cbr\u003e 3.6.4.2 Normalization\u003cbr\u003e 3.6.4.3 Progressive latent component training\u003cbr\u003e 3.6.4.4 Alternating applications of least squares and coordinate descent\u003cbr\u003e 3.6.4.5 User and Item Bias Integration\u003cbr\u003e 3.6.4.6 Integrating Implicit Feedback\u003cbr\u003e 3.6.5 Singular value decomposition\u003cbr\u003e 3.6.5.1 A Simple Iterative Approach to SVD\u003cbr\u003e 3.6.5.2 Optimization-based approach\u003cbr\u003e 3.6.5.3 Recommendations outside the sample\u003cbr\u003e 3.6.5.4 Example of Singular Value Decomposition\u003cbr\u003e 3.6.6 Factorization of nonnegative matrices\u003cbr\u003e 3.6.6.1 Advantages of Interpretability\u003cbr\u003e 3.6.6.2 Consideration of Factorization Using Implicit Feedback\u003cbr\u003e 3.6.6.3 Computational and Weighting Problems for Implicit Feedback\u003cbr\u003e 3.6.6.4 Ratings with both likes and dislikes\u003cbr\u003e 3.6.7 Understanding the matrix factorization series\u003cbr\u003e 3.7 Decomposition and Neighbor Model Integration\u003cbr\u003e 3.7.1 Baseline Estimation Model: Non-Personalized Bias-Centric Model\u003cbr\u003e 3.7.2 Neighboring parts of the model\u003cbr\u003e 3.7.3 Latent factor part of the model\u003cbr\u003e 3.7.4 Integration of neighboring and latent factors\u003cbr\u003e 3.7.5 Solving the Optimization Model  \u003cbr\u003e3.7.6 Considerations on Accuracy\u003cbr\u003e 3.7.7 Integrating latent factor models with random models\u003cbr\u003e 3.8 Summary\u003cbr\u003e 3.9 References\u003cbr\u003e 3.10 Practice Problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 4.\u003cbr\u003e Content-based recommendation system\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 4.1 Overview\u003cbr\u003e 4.2 Basic Components of a Content-Based System\u003cbr\u003e 4.3 Preprocessing and feature extraction\u003cbr\u003e 4.3.1 Feature Extraction\u003cbr\u003e 4.3.1.1 Example of Product Recommendation\u003cbr\u003e 4.3.1.2 Example of Web Page Recommendation\u003cbr\u003e 4.3.1.3 Example of music recommendation\u003cbr\u003e 4.3.2 Feature Representation and Refinement\u003cbr\u003e 4.3.3 Collecting user likes and dislikes\u003cbr\u003e 4.3.4 Selecting Map Features and Setting Weights\u003cbr\u003e 4.3.4.1 Gini coefficient\u003cbr\u003e 4.3.4.2 Entropy\u003cbr\u003e 4.3.4.3 X2-Statistics\u003cbr\u003e 4.3.4.4 Normalized deviation\u003cbr\u003e 4.3.4.5 Setting feature weights\u003cbr\u003e 4.4 User Profile Learning and Filtering\u003cbr\u003e 4.4.1 Nearest Neighbor Classification\u003cbr\u003e 4.4.2 Connecting with a Case-Based Recommendation System\u003cbr\u003e 4.4.3 Bayesian classification model\u003cbr\u003e 4.4.3.1 Intermediate probability estimation\u003cbr\u003e 4.4.3.2 Example of a Bayesian Model\u003cbr\u003e 4.4.4 Rule-based classification model\u003cbr\u003e 4.4.4.1 Example of a rule-based method\u003cbr\u003e 4.4.5 Regression-based models\u003cbr\u003e 4.4.6 Overview of Other Learning Models and Comparisons  \u003cbr\u003e4.4.7 Description of the Content-Based System\u003cbr\u003e 4.5 Content-Based vs. Collaborative Filtering Recommendations\u003cbr\u003e 4.6 Using Content-Based Models for Collaborative Filtering Systems\u003cbr\u003e 4.61 Using User Profiles\u003cbr\u003e 4.7 Summary\u003cbr\u003e 4.8 References\u003cbr\u003e 4.9 Practice Problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 5.\u003cbr\u003e Knowledge-based recommendation system\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 5.1 Overview\u003cbr\u003e 5.2 Constraint-based recommendation system\u003cbr\u003e 5.2.1 Returning Related Results\u003cbr\u003e 5.2.2 Interaction Methodology\u003cbr\u003e 5.2.3 Ranking matching items\u003cbr\u003e 5.2.4 Handling Unacceptable Results or Empty Sets\u003cbr\u003e 5.2.5 Add constraints\u003cbr\u003e 5.3 Case-Based Recommendations\u003cbr\u003e 5.3.1 Similarity Metrics\u003cbr\u003e 5.3.1.1 Incorporating Diversity into Similarity Calculations\u003cbr\u003e 5.3.2 Modification Methodology\u003cbr\u003e 5.3.2.1 Simple fixes\u003cbr\u003e 5.3.2.2 Complex Modifications\u003cbr\u003e 5.3.2.3 Dynamic Modification\u003cbr\u003e 5.3.3 Description of the modification\u003cbr\u003e 5.4 Continuous personalization of knowledge-based systems\u003cbr\u003e 5.5 Summary\u003cbr\u003e 5.6 References\u003cbr\u003e 5.7 Practice Problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 6.\u003cbr\u003e Ensemble-based and hybrid recommender systems\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 6.1 Overview\u003cbr\u003e 6.2 Ensemble methods from a classification perspective\u003cbr\u003e 6.3 Weighted Hybrid  \u003cbr\u003e6.3.1 Combining different types of models\u003cbr\u003e 6.3.2 Bagging Adaptation in Classification\u003cbr\u003e 6.3.3 Injection of Randomness\u003cbr\u003e 6.4 Switching Hybrid\u003cbr\u003e 6.4.1 Switching Mechanism for Cold Start Problems\u003cbr\u003e 6.4.2 Model Bucket\u003cbr\u003e 6.5 Cascade Hybrid\u003cbr\u003e 6.5.1 Continuous redefinition of recommendations\u003cbr\u003e 6.5.2 Boosting\u003cbr\u003e 6.5.2.1 Weighted Base Model\u003cbr\u003e 6.6 Feature Augmented Hybrid\u003cbr\u003e 6.7 Meta-Level Hybrid\u003cbr\u003e 6.8 Feature Combination Hybrid\u003cbr\u003e 6.8.1 Regression and Matrix Factorization\u003cbr\u003e 6.8.2 Meta-level features\u003cbr\u003e 6.9 Mixed Hybrid\u003cbr\u003e 6.10 Summary\u003cbr\u003e 6.11 References\u003cbr\u003e 6.12 Practice Problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 7.\u003cbr\u003e Recommender System Evaluation\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 7.1 Overview\u003cbr\u003e 7.2 Evaluation Paradigm\u003cbr\u003e 7.2.1 User Research\u003cbr\u003e 7.2.2 Online Assessment\u003cbr\u003e 7.2.3 Offline Evaluation Using Past Data Sets\u003cbr\u003e 7.3 General Goals of Evaluation Design\u003cbr\u003e 7.3.1 Accuracy\u003cbr\u003e 7.3.2 Coverage\u003cbr\u003e 7.3.3 Reliability and Trust\u003cbr\u003e 7.3.4 Novelty\u003cbr\u003e 7.3.5 Surprise\u003cbr\u003e 7.3.6 Diversity\u003cbr\u003e 7.3.7 Robustness and Stability\u003cbr\u003e 7.3.8 Scalability\u003cbr\u003e 7.4 Design Issues for Offline Recommendation Evaluation  \u003cbr\u003e7.4.1 Netflix Prize Dataset Case Study\u003cbr\u003e 7.4.2 Classification of training and test scores\u003cbr\u003e 7.4.2.1 Holdout\u003cbr\u003e 7.4.2.2 Cross-validation\u003cbr\u003e 7.4.3 Classification Design and Comparison\u003cbr\u003e 7.5 Accuracy indicators for offline evaluation\u003cbr\u003e 7.5.1 Measuring the accuracy of rating predictions\u003cbr\u003e 7.5.1.1 RMSE vs. MAE\u003cbr\u003e 7.5.1.2 The Influence of the Long Tail\u003cbr\u003e 7.5.2 Ranking Evaluation through Correlation\u003cbr\u003e 7.5.3 Ranking by Usefulness\u003cbr\u003e 7.5.4 Ranking Evaluation through Receiver Operational Characteristics\u003cbr\u003e 7.5.5 Which ranking metric is best?\u003cbr\u003e 7.6 Limitations of the Evaluation Measurement Scale\u003cbr\u003e 7.6.1 Preventing Evaluation Manipulation\u003cbr\u003e 7.7 Summary\u003cbr\u003e 7.8 References\u003cbr\u003e 7.9 Practice Problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 8.\u003cbr\u003e Context-sensitive recommendation system\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 8.1 Overview\u003cbr\u003e 8.2 Multidimensional Approach\u003cbr\u003e 8.2.1 The Importance of Hierarchy\u003cbr\u003e 8.3 Context Prefiltering: A Reduction-Based Approach\u003cbr\u003e 8.3.1 Ensemble-based improvements\u003cbr\u003e 8.3.2 Multi-stage estimation\u003cbr\u003e 8.4 Post-filtering methodology\u003cbr\u003e 8.5 Context-specific modeling\u003cbr\u003e 8.5.1 Neighbor-based methods\u003cbr\u003e 8.5.2 Latent Factor Model\u003cbr\u003e 8.5.2.1 Factoring Machine \u003cbr\u003e8.5.2.2 Generalization of the quadratic factorization machine\u003cbr\u003e 8.5.2.3 Other Applications of Latent Parameterization\u003cbr\u003e 8.5.3 Content-based model\u003cbr\u003e 8.6 Summary\u003cbr\u003e 8.7 References\u003cbr\u003e 8.8 Practice Problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 9.\u003cbr\u003e Time and location-sensitive recommendation systems\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 9.1 Overview\u003cbr\u003e 9.2 Temporal Collaborative Filtering\u003cbr\u003e 9.2.1 Recency-based model\u003cbr\u003e 9.2.1.1 Attenuation-based methods\u003cbr\u003e 9.2.1.2 Windows-based method\u003cbr\u003e 9.2.2 Periodic Context Processing\u003cbr\u003e 9.2.2.1 Pre-filtering and post-filtering\u003cbr\u003e 9.2.2.2 Direct inclusion of temporal context\u003cbr\u003e 9.2.3 Modeling Ratings as a Function of Time\u003cbr\u003e 9.2.3.1 Time-SVD++ Model\u003cbr\u003e 9.3 Discrete-time models\u003cbr\u003e 9.3.1 Markov Model\u003cbr\u003e 9.3.1.1 Selective Markov Model\u003cbr\u003e 9.3.1.2 Other Markov Alternatives\u003cbr\u003e 9.3.2 Sequential Pattern Mining\u003cbr\u003e 9.4 Location-Aware Recommendation Systems\u003cbr\u003e 9.4.1 Preferred Areas\u003cbr\u003e 9.4.2 Travel Area\u003cbr\u003e 9.4.3 Combination of Preferences and Travel Locations\u003cbr\u003e 9.5 Summary\u003cbr\u003e 9.6 References\u003cbr\u003e 9.7 Practice Problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 10.\u003cbr\u003e Recommended network structure\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 10.1 Overview\u003cbr\u003e 10.2 Ranking Algorithm\u003cbr\u003e 10.2.1 PageRank  \u003cbr\u003e10.2.2 Personalized PageRank\u003cbr\u003e 10.2.3 Applications to Neighborhood-Based Methods\u003cbr\u003e 10.2.3.1 Social Network Recommendations\u003cbr\u003e 10.2.3.2 Personalization of Heterogeneous Social Media\u003cbr\u003e 10.2.3.3 Traditional Collaborative Filtering\u003cbr\u003e 10.2.4 Similarity Rank\u003cbr\u003e 10.2.5 Relationship between Search and Recommendation\u003cbr\u003e 10.3 Recommendations by group classification\u003cbr\u003e 10.3.1 Iterative classification algorithm\u003cbr\u003e 10.3.2 Label propagation via random walk\u003cbr\u003e 10.3.3 Applicability of Collaborative Filtering to Social Networks\u003cbr\u003e 10.4 Friend Recommendations: Link Prediction\u003cbr\u003e 10.4.1 Neighborhood-based scale\u003cbr\u003e 10.4.2 Katz scale\u003cbr\u003e 10.4.3 Random walk-based scale\u003cbr\u003e 10.4.4 Link Prediction as a Classification Problem\u003cbr\u003e 10.4.5 Matrix Factorization for Link Prediction\u003cbr\u003e 10.4.5.1 Symmetric matrix factorization\u003cbr\u003e 10.4.6 The Connection Between Link Prediction and Collaborative Filtering\u003cbr\u003e 10.4.6.1 Using Link Prediction Algorithms in Collaborative Filtering\u003cbr\u003e 10.4.6.2 Link Prediction Using Collaborative Filtering Algorithms\u003cbr\u003e 10.5 Social Impact Analysis and Word-of-Mouth Marketing\u003cbr\u003e 10.5.1 Linear Threshold Model\u003cbr\u003e 10.5.2 Independent Cascade Model\u003cbr\u003e 10.5.3 Influence Function Evaluation  \u003cbr\u003e10.5.4 Target Influence Analysis Model for Social Streams\u003cbr\u003e 10.6 Summary\u003cbr\u003e 10.7 References\u003cbr\u003e 10.8 Practice Problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 11.\u003cbr\u003e Social and Trust-Centric Recommendation Systems\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 11.1 Overview\u003cbr\u003e 11.2 Multidimensional Models for Social Context\u003cbr\u003e 11.3 Network-Centric and Trust-Centric Methodologies\u003cbr\u003e 11.3.1 Data Collection for Building a Trust Network\u003cbr\u003e 11.3.2 Trust Propagation and Aggregation\u003cbr\u003e 11.3.3 Simple recommendation model without trust propagation\u003cbr\u003e 11.3.4 TidalTrust Algorithm\u003cbr\u003e 11.3.5 MoleTrust Algorithm\u003cbr\u003e 11.3.6 TrustWalker Algorithm\u003cbr\u003e 11.3.7 Link Prediction Methodology\u003cbr\u003e 11.3.8 Matrix Factorization Methodology\u003cbr\u003e 11.3.8.1 Improvements to the Logistic Function\u003cbr\u003e 11.3.8.2 Variations of the Social Trust Component\u003cbr\u003e 11.3.9 Advantages of Social Recommendation Systems\u003cbr\u003e 11.3.9.1 Recommendations for Controversial Users and Items\u003cbr\u003e 11.3.9.2 Usefulness of Cold Start\u003cbr\u003e 11.3.9.3 Attack Resistance\u003cbr\u003e 11.4 User Interactions in Social Recommendation Models\u003cbr\u003e 11.4.1 Expressing Folksonomy\u003cbr\u003e 11.4.2 Collaborative Filtering in Social Tagging Systems\u003cbr\u003e 11.4.3 Selecting meaningful tags  \u003cbr\u003e11.4.4 Social Tagging Recommendation Model without Rating Matrix\u003cbr\u003e 11.4.4.1 Multidimensional Methodology for Context-Sensitive Systems\u003cbr\u003e 11.4.4.2 Rank-based methodology\u003cbr\u003e 11.4.4.3 Content-based methodology\u003cbr\u003e 11.4.5 Social Tagging Recommendation Model Using Rating Matrix\u003cbr\u003e 11.4.5.1 Neighborhood-based approach\u003cbr\u003e 11.4.5.2 Linear Regression\u003cbr\u003e 11.4.5.3 Matrix Factorization\u003cbr\u003e 11.4.5.4 Content-based methods\u003cbr\u003e 11.5 Summary\u003cbr\u003e 11.6 References\u003cbr\u003e 11.7 Practice Problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 12.\u003cbr\u003e Attack Prevention Recommendation System\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 12.1 Overview\u003cbr\u003e 12.2 Understanding the Tradeoffs of Attack Models\u003cbr\u003e 12.2.1 Quantifying Attack Impact\u003cbr\u003e 12.3 Attack Types\u003cbr\u003e 12.3.1 Random Attacks\u003cbr\u003e 12.3.2 Average Attack\u003cbr\u003e 12.3.3 Bandwagon Attack\u003cbr\u003e 12.3.4 Popular Attacks\u003cbr\u003e 12.3.5 Love\/Hate Attacks\u003cbr\u003e 12.3.6 Reverse Bandwagon Attack\u003cbr\u003e 12.3.7 Detection Attack\u003cbr\u003e 12.3.8 Segment Attack\u003cbr\u003e 12.3.9 Effectiveness of the Basic Recommendation Algorithm\u003cbr\u003e 12.4 Attack Detection in Recommendation Systems\u003cbr\u003e 12.4.1 Detecting Personal Attack Profiles\u003cbr\u003e 12.4.2 Group Attack Profile Detection\u003cbr\u003e 12.4.2.1 Preprocessing Methods\u003cbr\u003e 12.4.2.2 Online method \u003cbr\u003e12.5 Strategies for Strong Recommendation Design\u003cbr\u003e 12.5.1 Preventing Automated Attacks Using CAPTCHA\u003cbr\u003e 12.5.2 Using Social Trust\u003cbr\u003e 12.5.3 Designing a Strong Recommendation Algorithm\u003cbr\u003e 12.5.3.1 Integrating Clustering into the Neighbor Method\u003cbr\u003e 12.5.3.2 Detecting fake profiles during recommended times\u003cbr\u003e 12.5.3.3 Association-based algorithms\u003cbr\u003e 12.5.3.4 Robust Matrix Factorization\u003cbr\u003e 12.6 Summary\u003cbr\u003e 12.7 References\u003cbr\u003e 12.8 Practice Problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 13.\u003cbr\u003e Advanced Topics in Recommender Systems\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 13.1 Overview\u003cbr\u003e 13.2 Ranking Learning\u003cbr\u003e 13.2.1 Pairwise Rank Learning\u003cbr\u003e 13.2.2 List Ranking Learning\u003cbr\u003e 13.2.3 Comparison with Ranking Learning Methods in Other Domains\u003cbr\u003e 13.3 Multi-arm bandit algorithm\u003cbr\u003e 13.3.1 Naive Algorithm\u003cbr\u003e 13.3.2 e-Greedy Algorithm\u003cbr\u003e 13.3.3 Offset (Upper Confidence Limit) Method\u003cbr\u003e 13.4 Group Recommendation System\u003cbr\u003e 13.4.1 Collaborative and Content-Based Systems\u003cbr\u003e 13.4.2 Knowledge-Based Systems\u003cbr\u003e 13.5 Multi-criteria Recommender Systems\u003cbr\u003e 13.5.1 Neighbor-based methods\u003cbr\u003e 13.5.2 Ensemble-based methods\u003cbr\u003e 13.5.3 Multi-criteria system without overall rating\u003cbr\u003e 13.6 Active Learning in Recommender Systems  \u003cbr\u003e13.6.1 Heterogeneity-based model\u003cbr\u003e 13.6.2 Performance-based model\u003cbr\u003e 13.7 Privacy Protection in Recommendation Systems\u003cbr\u003e 13.7.1 Condensation-based privacy protection\u003cbr\u003e 13.7.2 Challenges with High-Dimensional Data\u003cbr\u003e 13.8 Interesting Applications\u003cbr\u003e 13.8.1 Portal Content Personalization\u003cbr\u003e 13.8.1.1 Dynamic Profiler\u003cbr\u003e 13.8.1.2 Google News Personalization\u003cbr\u003e 13.8.2 Computerized Advertising vs. Recommendation Systems\u003cbr\u003e 13.8.2.1 The Importance of Multi-Arm Bandits\u003cbr\u003e 13.8.3 Mutual Recommendation System\u003cbr\u003e 13.8.3.1 Using Hybrid Methods\u003cbr\u003e 13.8.3.2 Using link prediction methods\u003cbr\u003e 13.9 Summary\u003cbr\u003e 13.10 References\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eTarget audience for this book\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e It includes numerous examples and practice problems to make it suitable for use as a textbook, and the basic topics and algorithms chapters are designed with lecture-based teaching in mind.\u003cbr\u003e And we have put a lot of effort into making it useful for application and reference by many industrial practitioners and researchers.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eStructure of this book\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Each chapter in this book is organized into three categories.\u003cbr\u003e\u003cbr\u003e 1. \u003cbr\u003eAlgorithms and Evaluation: Discusses the basic algorithms of recommender systems, including collaborative filtering methods, content-based methods, and knowledge-based methods.\u003cbr\u003e We also cover techniques for hybrid methods and evaluation of recommender systems.\u003cbr\u003e\u003cbr\u003e 2.\u003cbr\u003e Domain and Context-Specific Recommender Systems: The context of a recommender system plays a crucial role in providing effective recommendations.\u003cbr\u003e For example, a user looking for a restaurant might want to use their location data as additional context.\u003cbr\u003e The context of a recommendation can be seen as very important additional information that influences the goal of the recommendation.\u003cbr\u003e Different domain types, such as temporal data, spatial data, and social data, provide different types of context.\u003cbr\u003e We will also discuss the issues that arise when using social information to increase the reliability of the recommendation process.\u003cbr\u003e We also cover factorization machines and reliable recommendation systems.\u003cbr\u003e\u003cbr\u003e 3. \u003cbr\u003eAdvanced Topics and Applications: We explore various robustness aspects of recommender systems, such as sealing systems, attack models, and defense methods.\u003cbr\u003e Topics such as ranked learning, multi-arm bandits, group recommender systems, multi-criteria systems, and active learning systems are also discussed.\u003cbr\u003e We also examine several application environments in which it has been used, such as news recommendation systems, query search, and computerized advertising.\u003cbr\u003e The 'Applications' section provides ideas on how to apply the methods introduced in these previous chapters to other domains.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eAuthor's Note\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e As the Web became a vital medium for business and e-commerce, the importance of recommendation systems increased in the 1990s.\u003cbr\u003e The web offers unprecedented opportunities for personalization that other channels simply can't offer.\u003cbr\u003e In particular, we have provided a user interface that can be used for recommended items, adding convenience to data collection. \u003cbr\u003eSince then, recommendation systems have grown significantly in public awareness.\u003cbr\u003e Many conferences and workshops have been devoted exclusively to recommender systems, with the ACM conference being particularly noteworthy.\u003cbr\u003e Because he regularly contributes extensively to the latest results on recommender systems.\u003cbr\u003e The topic of recommendation systems is very diverse.\u003cbr\u003e Because when creating recommendations, you can specify different types, such as 'user-preferences' and 'user-requirements'.\u003cbr\u003e The most well-known methods in recommender systems are collaborative filtering, content-based, and knowledge-based methods.\u003cbr\u003e These three methods form the basis of research on recommender systems.\u003cbr\u003e In recent years, special methods have been designed for various data domains and contexts such as time, location, and social information. \u003cbr\u003eWe have proposed numerous advanced methods applicable to special scenarios and diverse application domains such as query log mining, news recommendation, and computational advertising.\u003cbr\u003e This book is structured to reflect these key themes.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eTranslator's Note\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 'Recommendation' is a methodology that can be utilized not only in certain industries but also in a wide variety of industries, and is especially sought after these days when data is just beginning to accumulate.\u003cbr\u003e However, it was difficult to find a book that introduced the situations in which recommended algorithms should be used and the pros and cons of each algorithm compared to their general usability.\u003cbr\u003e Moreover, it is even more difficult to find materials written in a language that can be understood by non-experts other than data engineers and data scientists. \u003cbr\u003eI believe this book will answer any questions you may have had, and as new ML models are emerging based on the basic algorithms introduced in the book, I believe it will be even more helpful in understanding today's recommendation algorithms.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e Park Hee-won\u003cbr\u003e\u003cbr\u003e While translating this book, I learned that recommendation systems are not just specific algorithms used in specific fields, but are being applied across all industries.\u003cbr\u003e I think it would be helpful for those who want to understand recommendation systems to use it as a basic book, as it provides theoretical explanations ranging from basic algorithm explanations to the latest algorithms that practitioners can utilize.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e Lee Joo-hee\u003cbr\u003e\u003cbr\u003e I first encountered this book in a recommendation systems class in graduate school. \u003cbr\u003eI wanted to learn more about recommendation systems, so I looked for related books in online bookstores, but I couldn't find any books that covered the theory of recommendation systems.\u003cbr\u003e So, I found this book among the original books that covers the theory of recommendation systems from the basics to the advanced level.\u003cbr\u003e This book provided a good opportunity to understand recommendation systems, but I was disappointed that there was no such book in Korea.\u003cbr\u003e So, I translated this book to help lower the barrier to entry for those who want to study, research, and apply recommendation systems in practice.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e Lee Jin-hyung \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e December 31, 2021\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 600 pages | 180*255*28mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791161755878\u003c\/div\u003e\n\n\u003cdiv 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