只是简单谢了一个Demo，先贴上GitHub地址。
https://github.com/wang135139/recommend-system

基本概念就不过多介绍了，相信能看明白的都了解。如果想了解相关推荐先做好知识储备：
1.什么事推荐算法
2.什么是基于邻域的推荐算法

笔者选用的是GroupLens的MoviesLens数据
传送门GroupLens

数据集处理

此处截取数据 UserId + MovieId 作为隐反馈数据。个人的实现方式并不是很好，之后再考虑优化，如果有好的想法欢迎小纸条。
基本设置项目结构如下：

/project
        /analyzer --推荐分析-CollaborativeFileringanalyzer
        /bean --数据元组-BasicBean
            -HabitsBean
        /input --输入设置-ReaderFormat
        /recommender --推荐功能-UserRecommender

首先思路是截取MovieLens数据，转化为格式化的书籍格式。MovieLens数据基本格式为

| user id | item id | rating | timestamp |

读取后的数据为表结构，实际可以用 Map 或者 二维数组 进行存储。
考虑到之后转化的问题，决定用二维数组。

设置BasicBean用于存储表结构中的行，主要设置List < String >用于存储一行数据中的单项数据

    /*** A row of data sets describes in witch the parameters are included.* * @author wqd * 2016/01/18*/public class BasicBean {private List<String> parameters;//  private int num;private boolean tableHead;///Default constructor,the row set n floders and is or not a table headpublic BasicBean(boolean head) {parameters = new ArrayList<String>();this.tableHead = head;}//Default constructor,the row set table head and how much the row //set is defined by the variable parameters,it isn't a table headpublic BasicBean(String... strings) {this(false, strings);}//Default constructor,the row set table head and how much the row //set is defined by the variable parameters and is or not a table headpublic BasicBean(boolean head, String... strings) {parameters = new ArrayList<String>();for(String string : strings) {parameters.add(string);}//      this.num = parameters.size();this.tableHead = head;}public int add(String param) {parameters.add(param);return this.getSize();}//replace a parameter value pointed to a new value//If success,return true.If not,return false.public boolean set(int index, String param) {if(index < this.getSize())parameters.set(index, param);elsereturn false;return true;}//Get the head.If it has table head,return ture.//If not,return flase;public boolean isHead() {return tableHead;}//Override toString()public String toString() {StringBuilder str = new StringBuilder(" ");int len = 1;for (String string : parameters) {str.append("\t|" + string);if(len++ % 20 == 0)str.append("\n");}return str.toString();}//Get number of parameterspublic int getSize() {return parameters.size();}//Get arraypublic List<String> getArray() {return this.parameters;}//Get ID of a setpublic int getId() {return this.getInt(0);}public String getString(int index) {return parameters.get(index);}public int getInt(int index) {return Integer.valueOf(parameters.get(index));}public boolean getBoolean(int index) {return Boolean.valueOf(parameters.get(index));}public float getFloat(int index) {return Float.valueOf(parameters.get(index));}}

在原数据读取之后，数据处理的话效率还是比较差，冗余字段比较多，因为一个用户会对多个电影反馈数据。因此，将
| user id | item id | rating | timestamp |
=>
| user id | item id 1 | item id 2 | item id 3 | item id 4 …

这边设置HabitsBean用于存储，单独将id进行抽取，直接存储在Bean中。实际在list中，存储user item ids，原因是在之后进行操作时，ID操作频繁。

public class HabitsBean extends BasicBean {private int id ;//get the IDpublic int getId() {return id;}//set the IDpublic void setId(int id) {this.id = id;}public HabitsBean() {this(-1);}//default id is -1,it means the id hadn't been evaluatedpublic HabitsBean(int id) {this.id = id;}//Override Object toString() methodpublic String toString() {StringBuilder str = new StringBuilder("HabitBean " + this.id + " :");str.append(super.toString());return str.toString();}}

将元组数据读取之后，再将元组数据进行压缩重组，转化为方便与处理的数据格式。设置ReaderFormat进行处理，Demo如下：

/*** This class for reading training and test files.It can * be suitable for Grouplens and other data sets.* @author wqd**/
public class ReaderFormat {List<BasicBean> lists;List<HabitsBean> formLists;public List<BasicBean> read (String filePath) throws IOException {@SuppressWarnings("resource")BufferedReader in = new BufferedReader(new FileReader(filePath));String s;BasicBean basicBean = null;lists = new ArrayList<BasicBean>();while((s = in.readLine()) != null) {
//          System.out.println(s);String[] params = s.split("\t");//          for (String string : params) {
//              System.out.println(string);
//          }basicBean = new BasicBean(params);lists.add(basicBean);}return lists;}//combine user log like | userID | habitID | ...//to userID and | habitID1 | habitID2 | habitID3 | ...//sort the userIDpublic List<HabitsBean> formateLogUser(String filePath) throws IOException {lists = this.read(filePath);formLists = new LinkedList<HabitsBean>();HabitsBean row = null;for (BasicBean basicBean : lists) {if(basicBean.) {row = new HabitsBean(1);row.setId(basicBean.getInt(0));row.add(basicBean.getString(1));formLists.add(row);} else {this.addBinarySerch(formLists, basicBean);}}return formLists;}//binary serchprivate void addBinarySerch(List<HabitsBean> lists, BasicBean bean) {int start = 0;int end = lists.size()-1;int pointer = (start + end + 1) / 2;HabitsBean row = lists.get(pointer);while(start <= end) {if(row.getId() == bean.getId()) {row.add(bean.getString(1));lists.set(pointer, row);return ;} else if(start == end) {break;}else if(row.getId() > bean.getId()) {end = pointer;} else if(row.getId() < bean.getId()) {start = pointer;}pointer = (start + end + 1) / 2;row = lists.get(pointer);}HabitsBean newBean = new HabitsBean(bean.getId());newBean.add(bean.getString(1));lists.add(newBean);return ;}// testpublic static void main(String[] args) {ReaderFormat readerFormat = new ReaderFormat();try {List<HabitsBean> lists = readerFormat.formateLogUser("E:/WorkSpace/Input/ml-100k/u1.base");for (HabitsBean habitsBean : lists) {System.out.println(habitsBean.toString());}} catch (IOException e) {// TODO Auto-generated catch blocke.printStackTrace();}}
}

推荐算法

协同过滤算法的核心思想是根据用户间的相似度，来进行推荐。
N(u)，N(v)表示u，v用户有过隐性反馈的集合，Jaccard公式

或者采用余弦相似度