案例实战需求之大数据下的用户画像标签去重

介绍

用户画像 英文为User Profile，是根据用户基本属性、社会属性、行为属性、心理属性等真实信息⽽抽象出的⼀个标签化的、虚拟的⽤户模型。“⽤户画像”的实质是对 “⼈”的数字化。应⽤场景有很多，比如个性化推荐、精准营销、⾦融⻛控、精细化运营等等， 举个例⼦来理解⽤户画像的实际应⽤价值，我们经常⽤⼿机⽹购，淘宝⾥⾯的千⼈千⾯通过“标签 tag”来对⽤户的多维度特征进⾏提炼和标识，那每个⼈的⽤户画像就需要存储，set集合就适合去重⽤户画像不⽌针对某个⼈，也可以某⼀⼈群或⾏业的画像，利⽤redis可以很好的去重

@SpringBootTest
class XdclassRedisApplicationTests {

	@Autowired
	private RedisTemplate redisTemplate;

	@Test
	public void userProfile(){

		BoundSetOperations operations = redisTemplate.boundSetOps("user:tags:1");

		operations.add("car","student","rich","dog","guangdong","rich");

		Set<String> set1 = operations.members();
		System.out.println(set1);

		operations.remove("dog");
		Set<String> set2 = operations.members();
		System.out.println(set2);
	}
}

社交应用里面的知识，关注、粉丝、共同好友案例

@SpringBootTest
class XdclassRedisApplicationTests {

	@Autowired
	private RedisTemplate redisTemplate;
	/**
	 * 社交应用
	 */
	@Test
	public void testSocial(){

		BoundSetOperations operationsLW = redisTemplate.boundSetOps("user:lw");
		operationsLW.add("A","B","C","D","E");
		System.out.println("老王的粉丝："+operationsLW.members());


		BoundSetOperations operationsXD = redisTemplate.boundSetOps("user:xd");
		operationsXD.add("A","B","F","G","H","K","J","W");
		System.out.println("小d的粉丝："+operationsXD.members());

		//差集
		Set lwSet = operationsLW.diff("user:xd");
		System.out.println("老王的专属用户："+lwSet);

		//差集
		Set xdSet = operationsXD.diff("user:lw");
		System.out.println("小D的专属用户："+xdSet);

		//交集
		Set interSet = operationsLW.intersect("user:xd");
		System.out.println("同时关注了两个人的用户："+interSet);

		//并集
		Set unionSet = operationsLW.union("user:xd");
		System.out.println("两个人的并集："+unionSet);

		//判断A用户是不是老王的粉丝
		boolean flag = operationsLW.isMember("A");
		System.out.println("A用户是不是老王的粉丝:"+flag);
	}
}