举荐算法是会常常遇到的技术。次要解决的是问题是：如果你喜爱书 A，那么你可能会喜爱书 B。
本文咱们应用 MySQL ，基于数据统计，拆解实现了一个简略的举荐算法。
首先，创立一个 用户喜爱的书数据表，所示意的是 user\_id 喜爱 book\_id。
CREATE TABLE user_likes ( user_id INT NOT NULL, book_id VARCHAR(10) NOT NULL, PRIMARY KEY (user_id,book_id), UNIQUE KEY book_id (book_id, user_id));CREATE TABLE user_likes_similar ( user_id INT NOT NULL, liked_user_id INT NOT NULL, rank INT NOT NULL, KEY book_id (user_id, liked_user_id));插入4条测试数据
INSERT INTO user_likes VALUES (1, 'A'), (1, 'B'), (1, 'C');INSERT INTO user_likes VALUES (2, 'A'), (2, 'B'), (2, 'C'), (2,'D');INSERT INTO user_likes VALUES (3, 'X'), (3, 'Y'), (3, 'C'), (3,'Z');INSERT INTO user_likes VALUES (4, 'W'), (4, 'Q'), (4, 'C'), (4,'Z');代表的含意为：用户 1 喜爱A、B、C，用户 2 喜爱 A、B、C、D，用户 3 喜爱 X、Y、C、Z，用户 4 喜爱 W、Q、C、Z。
认为用户 1 计算举荐书籍为例，咱们须要计算用户 1 和其余用户的类似度，而后依据类似度排序。
清空类似度数据表
DELETE FROM user_likes_similar WHERE user_id = 1;计算用户类似度数据表
INSERT INTO user_likes_similarSELECT 1 AS user_id, similar.user_id AS liked_user_id, COUNT(*) AS rank FROM user_likes target JOIN user_likes similar ON target.book_id= similar.book_id AND target.user_id != similar.user_id WHERE target.user_id = 1 GROUP BY similar.user_id ;能够看到查找到的类似度后果为
user_id, liked_user_id, rank1, 2, 21, 3, 11, 4, 1而后依据类似度排序，取前 10 个，就是举荐的书籍了。
SELECT similar.book_id, SUM(user_likes_similar.rank) AS total_rank FROM user_likes_similar JOIN user_likes similar ON user_likes_similar.liked_user_id = similar.user_id LEFT JOIN user_likes target ON target.user_id = 1 AND target.book_id = similar.book_id WHERE user_likes_similar.user_id = 1 AND target.book_id IS NULL GROUP BY similar.book_id ORDER BY total_rank desc LIMIT 10;