Recommendation System Practice

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With the development of information technology and the Internet, people gradually entered the era of information overload from the era of information scarcity. In this era, both information consumers and information producers are faced with great challenges: for information consumers, it is very difficult to find the information they are interested in from a large amount of information; For information producers, it is also a very difficult thing to make the information they produce stand out and attract the attention of users. Recommendation system is an important tool to solve this contradiction. The task of recommendation system is to contact users and information. On the one hand, it helps users find valuable information for themselves, and on the other hand, it enables information to be displayed in front of users who are interested in it, so as to achieve a win-win situation between information consumers and information producers.

Contents Chapter 1 A Good Recommendation System 1.1 What is a Recommendation system 1.2 The Application of personalized Recommendation System 1.2.1 E-commerce 1.2.2 Movie and video websites 1.2.3 Personalized Music Online radio 1.2.4 Social Networks 1.2.5 Personalized reading 1.2.6 Location-based Services 1.2.7 Personalized Mail 1.2.8 Personalized Advertising 1.3 Recommendation system evaluation 1.3.1 Experimental methods of recommendation system 1.3.2 Evaluation Indicators 1.3.3 Evaluation Dimensions Chapter 2 Using user behavior data 2.1 Introduction to User behavior data 2.2 User behavior analysis 2.2.1 Distribution of user activity and item popularity 2.2.2 Relationship between user activity and item popularity 2.3 Experimental design and algorithm evaluation 2.3.1 Data set 2.3.2 Experimental design 2.3.3 Evaluation indicators 2.4 Neighborhood based algorithm 2.4.1 User-based collaborative filtering algorithm 2.4.2 Item-based collaborative filtering algorithm 2.4.3 Comprehensive comparison between UserCF and ItemCF 2.5 Cryptic model 2.5.1 Basic Algorithm 2.5.2 Examples of actual systems based on LFM 2.5.3 Comparison between LFM and neighborhood based methods 2.6 Graph-based model 2.6.1 Dichotomized graph representation of user behavior data 2.6.2 Graph-based recommendation algorithm Chapter 3 Recommendation system cold start problem 3.1 Introduction to cold start problem 3.2 Using user registration information 3.3 Selecting appropriate items to initiate user interest 3.4 Using item content information 3.5 Playing the role of experts Chapter 4 Using user tag data 4.1 UGC tagging system representative application 4.1.1 Delicious 4.1.2 CiteULike 4.1.3 Last.fm 4.1.4 Douban 4.1.5 Hulu 4.2 Recommendations in the tagging system 4.2.1 Why do Users label 4.2.2 How Do Users Label 4.2.3 What Kind of Label Do Users Label 4.3 Label-based Recommendation System 4.3.1 Experimental Settings 4.3.2 A Simplest Algorithm 4.3.3 Algorithm Improvement 4.3.4 Graph-based Recommendation Algorithm 4.3.5 Label Based Recommendation 4.4 Recommending Labels to Users 4.4.1 Why To Recommend Labels to Users 4.4.2 How To Recommend Labels to Users 4.4.3 Experimental Settings 4.4.4 Graph-based Label Recommendation Algorithm 4.5 Extended Reading Chapter 5 Using Context Information 5.1 Time Context Information 5.1.1 Introduction to Time Effects 5.1.2 Examples of Time effects 5.1.3 Analysis of system time characteristics 5.1.4 Real-time performance of the recommendation system 5.1.5 Temporal diversity of the recommendation algorithm 5.1.6 Time-context recommendation algorithm 5.1.7 Time frame model 5.1.8 Offline experiment 5.2 Using Social Network Data 6.1 Access to social Network data 6.1.1 Email 6.1.2 User registration information 6.1.3 User location data 6.1.4 Forums and discussion groups 6.1.5 Instant messaging tools 6.1.6 Social networking Sites 6.2 Social Network Data Introduction 6.3 Recommendation based on social network 6.3.1 Neighborhood based social recommendation algorithm 6.3.2 Graph based social recommendation algorithm 6.3.3 Social recommendation algorithm in the actual system 6.3.4 Social recommendation system and collaborative filtering recommendation system 6.3.5 Information flow recommendation 6.4 Recommending friends to Users 6.4.1 Content-based matching 6.4.2 Friend Recommendations based on common interests 6.4.3 Friend Recommendations based on Social Network Graphs 6.4.4 Comparison of friend recommendation algorithms based on user surveys 6.5 Extended reading Chapter 7 Recommendation System Examples 7.1 Peripheral Architecture 7.2 Recommender System Architecture 7.3 Recommender Engine Architecture 7.3.1 Generating user feature Vectors 7.3.2 Features? Article related Recommendations 7.3.3 Filtering module 7.3.4 Ranking module 7.4 Extended reading Chapter 8 Scoring Prediction problems 8.1 Off-line experimental methods 8.2 Scoring prediction algorithms 8.2.1 Average 8.2.2 Neighborhood based methods 8.2.3 Implicit and matrix decomposition models 8.2.4 Add time information 8.2.5 model fusion 8.2.6 Netflix Prize related experimental results postscriptCopy the code

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