movielens

User_feature*t (item_feature), T () represents transpose, but now to consider a variety of bias, as follows:Where a represents the average of all scores, AI indicates that user i's BIAS,BJ represents the deviation of item J, and the Matrix multiplied is the same as the above meaning.So this time the expected formula and derivation of the formula as follows (here only the derivation of bias, matrix derivation or the same as above):Of course, the light said no practice false bashi, we chose the l

User movie scoring data set downloadhttp://grouplens.org/datasets/movielens/1) item-based, non-personalized, everyone sees the same2) user-based, personalized, everyone sees not the sameAfter the user's behavior analysis gets the user's liking, can calculate the similar user and the item according to the user's liking, then may base on the similar user or the item to recommend. This is the two branches in collaborative filtering, based on user-and ite

/tmpWget http://archive.ics.uci.edu/ml/databases/synthetic_control/synthetic_control.data Hadoop FS-mkdir testdataHadoop FS-put synthetic_control.data testdataHadoop FS-LSR testdata Hadoop cluster to execute Clustering AlgorithmsCD/usr/local/mahout Mahout org. Apache. mahout. Clustering. syntheticcontrol. canopy. JobMahout org. Apache. mahout. Clustering. syntheticcontrol. kmeans. JobMahout org. Apache. mahout. Clustering. syntheticcontrol. fuzzykmeans. JobMahout org. Apache. mahout. Clustering.

Events SELECT a.* from profiles a;# GROUP byhive> F ROM invites a INSERT OVERWRITE TABLE events SELECT A.bar, COUNT (*) WHERE a.foo > 0 GROUP by a.bar;# joinhive> from P Okes T1 JOIN invites t2 on (T1.bar = t2.bar) INSERT OVERWRITE TABLE events SELECT T1.bar, T1.foo, t2.foo;# multitable Inse Rtfrom src Insert OVERWRITE table dest1 Select src.* WHERE src.key Hive Usage Example Preparing the data source [[emailprotected] ~]# wget http://files.grouplens.org/datasets/

/value ing table 935.2 access data 965.2.1 access document 96 with MongoDB5.2.2 use hbase to access data 975.2.3 query redis 985.3 Update and delete data 985.3.1 update and modify data using MongoDB, hbase, and redis 985.3.2 limited atomicity and transaction integrity 995.4 Conclusion 100Chapter 1 query nosql storage 6th6.1 similarity between SQL and MongoDB query functions 1016.1.1 load movielens data 1036.1.2 mapreduce 108 in MongoDB6.2 access data

1. nmf-based Recommendation algorithmIn a recommender system such as Netflix or Movielens, there are two collections of users and movies. Give each user a score on some of the films, hoping to predict the user's rating of the other not seen the movie, so that it can be recommended according to the score value. The relationship between the user and the movie can be represented by a matrix, each column represents the user, each row represents a movie, a

sites on the Internet, which have already applied this technology to the user's more intelligent recommendation content. If you want to study collaborative filtering, you must not Miss Movielens (http://movielens.umn.edu/).　　It is one of the most famous research projects in collaborative filtering. The first generation of collaborative filtering technology, also known as user-based (user-based) collaborative filtering. Based on user's collaborative f

Taipanegative sample/positive sample ratio ratio for training setMovielens Data Set DownloadVerifying LFM validity using the Movielens data setInfluence of proportional parameter ratio of positive and negative sampleSeveral indicatorsAdvantages and disadvantages of LFMA typical machine learning algorithm, with a good mathematical basis, looks more mathematical aestheticindicators are generally slightly higher than ITEMCF and USERCFUse less memory dur

should be compared to the item I hit the score of 0.5 points, it is 2.5 points.Because the thought is so simple, so we come to practice a, of course, here is the most simple implementation, just to detect how the algorithm effect ... Data set is the same as the above blog, with a small data set inside the Movielens, which has 1000 users of 2000 items scored, 80% for training, 20% for testing.The specific code is as follows:#include The experimental r

deviations.I used the paper "A Guide to Singular Value decomposition for collaborative Filtering, a single-machine version of SVD matrix decomposition Prediction Score is realized.Https://github.com/linger2012/svd-for-recommendation-implemented-by-javaThe loss function used isSolution with SGD, the model is updated once for each known User-item score.1000-time traversal training set, for the test set of Rmse can reach 0.96, is still good.The data set used is one of the movielens.Both the code a

Data Description:movielens DataSet, which contains 100K movie scores from 943 users and a select 1682 movies . score at least 20 movies per user, data type User ID | item ID | rating | timestamp. Address: https://grouplens.org/datasets/movielens/1. Introduction of Pandas,numpy Package2, read the data: First, if the file is not in the default path, you need to change the path, using the following two lines of command, in addition to pay attention to

First, the premise prepares the 1.R language pack: Ggplot2 package (Drawing), Recommenderlab package, reshape package (data processing) 2. Get data: You can download these free data sets at the University of Minnesota's Social Computing Research Center website, which links to HT tp://grouplens.org/datasets/movielens/, can also be downloaded through the network disk Https://yunpan.cn/Oc6R9apvCnVXGcAccess password E1AF. This includes a dataset and a des

the E-commerce website, the intrinsic link between the commodity has the influence to the user's purchase behavior to be more remarkable. When used in recommendations, these two directions are also referred to as user based and object based. The content of this article is user based.recommended examples of film review The main content of this article is based on the similarity of user preferences to recommend items, the use of data sets for the Grouplens research collection from the late 1990s

For a machine learning system, there are several problems to be solved: 1, how to choose Feature. 2, which algorithm to choose. 3, how to set the parameters for this algorithm. Together, these questions are "how to choose a model". For example: can realize the classification system algorithm has one-vs-all logistic regression,neural NETWORK,SVM and so on, we should use which one. To solve this problem, we need to use different combinations (including algorithms, parameters, feature) on the dat