coursera recommender systems

reason, for example, we want to predict the user U on the item I score, we find the user U evaluation of other k items, with the same method weighted to get u to i prediction value. Generally speaking, the KNN model based on the user and based on the items should be considered at the same time, someone has done this research, reference papers: [Tag-aware recommender Systems by Fusion of collaborative filte

About recommender systems Recommender systems are software applications that aim to support users in their demo-making while interacting with large information spaces. they recommend items of interest to users based on preferences they have expressed, either explicitly or implicitly. the ever-expanding volume and incre

injecting data. -Personally suspect, how did the previous five methods inject data? It is difficult to directly modify the background database of others?! Clickstream attacks usually affect the "many of the classmates who read the book read the book."Attack countermeasure 1. Increase data injection costs by 2. Automatic detection of abnormal data by different systems, such as: In a short time in the same direction large amounts of data entry, compare

16.1 problem formalization16.2 Content-based recommender system16.3 Collaborative Filtering16.4 Collaborative filtering algorithm16.5 vectorization: Low-rank matrix decomposition16.6 Implementation of work Details: Normalization of the mean value 16.1 problem formalization 16.2Content-based recommender system 16.3Collaborative Filtering 16.4Collaborative filtering algorithm 16.

improve the quality of recommendation systems to help users mine and transmit associations. If both users read or love similar books but are not the same, their associations will be lost. Huang's paper shows that a diffusion activation algorithm can be used to help the recommendation system, especially to give appropriate recommendations to new users. Deshpande karypi: a record-based recommendation system is used to solve the Top N Problem in t

This week I saw chapter 6. The book consists of 25 chapters. From the point of view, this book provides a comprehensive introduction to the recommendation system, and also introduces some specificAlgorithm. There are some mathematical symbols in these formulas that I can't remember. The following is a summary of the first six chapters:Chapter 1: Introduction to the book;Chapter 2: Data mining methods used in recommendation systems, divided into: Dat

efficient algorithm this doesn ' t need to go back and forth between the X's and the Thetas, but that can solve for th ETA and X simultaneously}Collaborative Filteringoptimization ObjectiveNote:1. Sum over J says, for every user, the sum of all the movies rated by, user.for every movie I,sum over a ll the users J that has rated that movie.2. Just something over all the user movie pairs for which has a rating.3. If you were to hold the X's constant and just minimize with respect to the thetas th

defined by the attributes of the relevant characteristics, and the system is based on the characteristics of the user's evaluation object, learns the user's interest, and examines the matching degree between the user data and the project to be predicted. The user's data model depends on the learning method used, the decision tree, the neural network and the vector-based representation method. Content-based user data is a historical data that requires a user, and the user profile model may chang

Recommender systems handbook 6 This week I saw Chapter 1. The book consists of 25 chapters. From the point of view, this book provides a comprehensive introduction to the recommendation system, and also introduces some specificAlgorithm. There are some mathematical symbols in these formulas that I can't remember. The following is a summary of the first eight chapters:Chapter 1: Introduction to the book;C

Recommended systems (Recommender system) problem formulation:Recommendersystems: Why it has two reasons: first it is a very important machine learning application direction, in many companies occupy an important role, such as Amazon and other sites are very good to establish a recommendation system to promote the sale of goods. Secondly, the system has some big idea in machine learning, learning the big ide

descriptionAdvantages:(1) There is no need for large-scale users (like collaborative filtering) to get the relationship between items(2) Once the property of the item is obtained, the item can be recommended to the user immediately.3. Knowledge-based recommendationsIn some areas, such as the consumer electronics sector (e.g), the vast majority of data is a single purchase record. If you apply both of these methods, the data is too sparse to even get the recommended results. What if we have to r

understand, seemingly and the above--when a feature OK, use this feature; , the weaker features are used2. Parallel hybrid design Multiple recommendation engines, how to fuse together? 2.1 Cross-mixing multiple results of multiple recommendation engines, cross-merge into one result: first engine first result ranked first, second engine first result ranked second ... 2.2 Weighted mixed linear weighted combination, one weight per engine, weight normalization 2.3 switching mix when in some cases w

Basic idea: The data is divided into training set and test set, training model with training set data, test model with test set data. The Division of Training set and test set can be by the dimension of time, or by the dimension of the crowd. Risk: There may be biases for some methods.Using historical data to evaluate the data into training set test set and N-fold cross-validation according to Time dimension.There is also the direct use of human evaluation. However, the cost is larger, not on th