Detailed introduction to recommendation system

Recommendation systems often need to process data like user_id, item_id, rating, which are actually sparse matrices in mathematics. Scipy provides the sparse module to solve this problem, but scipy.sparse has many problems that are not suitable for use: 1 , cannot support data[i, ...], data[..., j], data[i, j] fast slicing at the same time; 2. Since the data is stored in memory, it cannot support massive data well. deal with. To support fast slicing of data[i, ...], data[..., j], the data of i or j needs to be stored centrally; at the same time, in order to save massive data, part of the data also needs to be placed on the hard disk. , use memory as buffer. The solution here is relatively simple. Use a Dict-like thing to store data. For a certain i (such as 9527), its data is stored in dict['i9527']. Similarly, for a certain j (such as 3306) , all its data is stored in dict['j3306'], which needs to be

1. A sparse matrix Python storage scheme that saves memory

Introduction: Recommendation systems often need to process data like user_id, item_id, rating, which are actually sparse matrices in mathematics, in scipy The sparse module is provided to solve this problem

2. Article recommendation system (2)_PHP tutorial

Introduction: Article recommendation system (2). ======APPRE.PHP========== $strlen=strlen($articlemsg); if($strlen50){ echo table align=center width=100%; echo tr align=centertd; echo, are you irritating me? In order to prevent some netizens from being friendly

3. Article Recommendation System (3)_PHP Tutorial

Introduction: Article Recommendation System (three). =====Article.php==== ? if(!isset($pagenum)){ $pagenum=1;} $conn=mysql_connect(localhost,user,password); mysql_select_db(bamboo); $sql=select count(*) from article; $result=mysql_que

4. Article recommendation system (3)

Introduction : Article recommendation system (3). =====Article.php==== ? if(!isset($pagenum)){ $pagenum=1;} $conn=mysql_connect(localhost,user,password); mysql_select_db(bamboo); $sql=select count(*) from article; $result=mysql_que

5. Mahout builds a book recommendation system

Introduction: This series of articles on the Hadoop family mainly introduces Hadoop family products. Commonly used projects include Hadoop, Hive, Pig, HBase, Sqoop, Mahout, Zookeeper, Avro, Ambari, Chukwa. Newly added projects include YARN, Hcatalog, Oozie, Cassandra, Hama. , Whirr, Flume, Bigtop, Crunch, Hue, etc. Started in 2011

6. Java class for union-finding of big data (based on HBase)

Introduction: When doing a recommendation system, I want to see how many categories naturally exist in the original data set, that is, find some subsets. These subsets belong to the original data set. There is no correlation between the subsets, and all the data within the subsets All are directly or indirectly related. The first consideration is that due to the size of the data, it is impossible to read it into the memory, so we have to use the hard disk (although very reluctantly)

7. Storm stream computing from entry to proficiency Technical articles (high concurrency strategy, batch transactions, Trid

Introduction: If you are interested in this course, you can add me at qq2059055336 to contact me. What is Storm? Why learn Storm ? Storm is Twitter's open source distributed real-time big data processing framework, which is called the real-time version of Hadoop in the industry. As more and more scenarios cannot tolerate the high latency of Hadoop's MapReduce, such as website statistics, recommendation systems, early warning systems, and financial services.

8. Error when switching from ms2000 to 2005: Microsoft][SQLServer 2000 Driv

Introduction: Reprint Address: http://www.shamoxia.com/html/y2010/2249.html Recently, a personalized paper recommendation system was developed for an older database. Since the system is relatively old, the database platform used is still sqlserver2000. Now everyone In fact, they are already using 2005 or 2008 or even higher versions, but in order to be compatible with the system, we

9. The recommendation system I wrote. Ha ha. You can guess what the form is like

#Introduction: I wrote a recommendation system. Ha ha. You can guess what the form is like. None INSERT INTO recommend (SELECT ut.userid,it.itemid, NOW() FROM user_tag ut,item_tag it WHERE EXISTS( SELECT it.tagid FROM item_tag it WHERE it.tagid IN (SELECT ut.tagid FROM user_tag ut)))

10. Friend recommendation based on tensor decomposition in social networks

Introduction: Based on tensor decomposition in social networks Friend recommendation Abstract Introduction Related research questions Description of the proposed friend recommendation method Experimental verification Conclusion Abstract The rapid growth of users in social networks poses challenges to existing friend recommendation systems. In this article, we use the tensor decomposition model to propose a new recommendation framework based on the user's tag behavior information to solve the problem of friends in social networks

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