A detailed introduction to the recommendation system

Recommendation system often need to deal with similar user_id, item_id, rating such data, in fact, is the sparse matrix in mathematics, SCIPY provides a sparse module to solve this problem, but scipy.sparse a lot of problems are not very suitable: 1, Not very good at the same time support data[i, ...], data[..., j], Data[i, j] fast slicing; 2, because the data is stored in memory, not very good support for massive data processing. To support Data[i, ...], data[..., j] fast slicing, need I or J of the data set storage, and in order to save a huge amount of data, also need to put part of the data on the hard disk, memory buffer. The solution here is simple, with a class dict to store the data, for a certain I (such as 9527), its data is stored in the dict[' i9527 '), the same, for a J (such as 3306), its entire data stored in the dict[' j3306 ') inside, Need to take

1. A sparse matrix Python storage scheme for comparing memory

Introduction: The recommendation system often need to deal with similar user_id, item_id, rating such data, in fact, is the sparse matrix in mathematics, SCIPY provides a sparse module to solve the problem

2. Article recommendation System (ii) _php tutorial

Introduction: Article recommendation System (II). ======appre. php========== $strlen =strlen ($articlemsg); if ($strlen) {echo table align=center width=100%; Echo tr ALIGN=CENTERTD; Echo, are you watering? To prevent some web-friendliness

3. Article recommendation System (iii) _php tutorial

Introduction: Article recommendation System (III). =====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 (III)

Introduction: Article recommendation System (III). =====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 building a book recommendation system

Introduction: The Hadoop family of articles, mainly about the Hadoop family of products, commonly used projects include Hadoop, Hive, Pig, HBase, Sqoop, Mahout, Zookeeper, Avro, Ambari, Chukwa, new additions include , YARN, Hcatalog, Oozie, Cassandra, Hama, Whirr, Flume, Bigtop, Crunch, hue, etc. Open from 2011

6. Java classes for large data collection (HBase-based)

Introduction: When making a referral system, you want to see how many types of classes naturally exist in the original dataset, that is, to find subsets that belong to the original dataset, that have no association between the subsets, and that all data within a subset is directly or indirectly related. The first thing to consider is that because of the size of the data, it is impossible to read into memory, so use the hard drive (though reluctantly)

7. Storm stream computing from getting started to mastering technology (high concurrency policies, batch transactions, Trid

Introduction: Interested in this course can add me qq2059055336 contact me what is storm? Why study storm? Storm is the open-source distributed real-time data processing framework of Twitter, which is called real-time Hadoop by the industry. As more and more scenarios are tolerated for the mapreduce high latency of Hadoop, such as website statistics, recommender systems, early warning systems, gold

8. Go from ms2000 to 2005 error: Microsoft][sqlserver-Driv

Introduction: Reprinted Address: Http://www.shamoxia.com/html/y2010/2249.html recently developed a personalized paper recommendation system for a relatively old database, because the system is relatively old, Use of the database platform or sqlserver2000, we have now actually used 2005 or 2008 or even higher version, but in order to be compatible with the system, we

9. I wrote the recommendation system. Ha ha. form, guess what?

Introduction: I wrote the recommendation system. Ha ha. form, guess what. No INSERT to 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. A friend referral based on tensor decomposition in social networks

Introduction: A Friend referral summary based on tensor decomposition in social networks introduction related research problem description The recommended method of the friend proposed experiment verification conclusion abstract the fast growing users in social networks challenge the existing friend referral system. In this paper, we use tensor decomposition model to propose a new framework based on user's tag behavior information to solve the social network friends

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