A caching mechanism to implement efficient e-commerce recommendation algorithms in Golang.

With the booming development of e-commerce business, recommendation algorithms have become one of the keys to competition among major e-commerce platforms. As an efficient and high-performance language, Golang has great advantages in implementing e-commerce recommendation algorithms. However, while implementing efficient recommendation algorithms, the caching mechanism is also an issue that cannot be ignored. This article will introduce how to implement the caching mechanism of efficient e-commerce recommendation algorithm in Golang.

1. Why a caching mechanism is needed

In the e-commerce recommendation algorithm, the generation of recommendation results requires a large amount of computing resources. For high-concurrency e-commerce platforms, each recommendation Recalculation is obviously unrealistic. In order to solve this problem, a caching mechanism can be used to cache the calculated recommendation results into memory for subsequent requests to avoid repeated calculations.

In addition, the e-commerce field needs to face a large amount of real-time data. Not only user behavior data needs to be updated in real time, but also product status, price, inventory and other information need to be updated in real time. Therefore, the caching mechanism can effectively solve the problem of data update and avoid the inconsistency between the cached data and the actual data due to data changes, thereby ensuring the accuracy of the recommendation results.

2. How to implement the caching mechanism

1. Select caching tools

Golang provides a variety of caching tools, including built-in map, sync.Map and Third-party libraries such as gcache, go-cache, etc. Among them, sync.Map is a newly added concurrency-safe Map in Golang 1.9 version. It can ensure safe reading and writing in a high-concurrency environment, and its performance is also very good. Therefore, this article uses sync.Map as an example.

2. Choose cache granularity based on business needs

When implementing the caching mechanism, it is necessary to select the cache granularity based on the characteristics of the e-commerce business to achieve the optimal caching effect. Under normal circumstances, the cache granularity of e-commerce recommendation algorithms can be refined to the following levels:

a. User-level cache

Cache the user's historical behavior, such as browsing records, purchases Records, collection records, etc. During each recommendation, recommendations are made based on the user's behavioral data to avoid double counting. Since each user's behavioral data is different, this method can provide more accurate recommendations.

b. Product level caching

Cache the basic information of the product, such as price, inventory, status, description, etc., and cache the relevant attributes of the product, such as brand, model, specification, material wait. During each recommendation, recommendations are made based on the attribute information of the product to avoid double counting.

c. Category level caching

Categories products according to categories and caches the product ID under each category. During each recommendation, recommendations are made based on the product ID under the current category to avoid double counting. This method is suitable for situations where there are many products in the same category.

3. Caching strategy

When implementing the caching mechanism of the e-commerce recommendation algorithm, it is necessary to formulate an appropriate caching strategy based on business needs. Usually, the LRU (Least Recently Used) cache elimination strategy can be adopted, that is, when the cache space is insufficient, the least recently used cache data is eliminated. At the same time, you can also set the cache expiration time, and the cached data will be automatically eliminated when it has not been accessed for a certain period of time. This ensures the timeliness and accuracy of cached data.

3. Example: Implementing e-commerce recommendation algorithm based on Golang’s caching mechanism

In this section, we will take the user-level caching strategy as an example to describe how to implement e-commerce recommendation in Golang. Algorithm caching mechanism.

1. Cache structure definition

Define a structure UserCache, including cache results, expiration time, usage time and other information.

type UserCache struct {

Data         []int               // 缓存的推荐结果
ExpiredTime  time.Time           // 过期时间
LastUsedTime time.Time           // 上次使用时间

}

2. Initialize the cache

Use sync.Map to initialize the cache and use the user ID as the key , UserCache caches as value.

var userCache sync.Map // Use sync.Map to initialize user-level cache
func main() {

// 缓存用户推荐结果
userID := 10001
res := []int{2001, 2002, 2003}
cacheTime := 10 * time.Minute   // 缓存时间为10分钟
setUserCache(userID, res, cacheTime)

}

func setUserCache(userID int, res []int, cacheTime time.Duration) {

userCache.Store(userID, UserCache{
    Data:         res,
    ExpiredTime:  time.Now().Add(cacheTime),
    LastUsedTime: time.Now(),
})

}

3. Get cache

At each recommendation, first check whether There is a calculated recommendation result. If it exists, the cached result will be returned directly. Otherwise, real-time calculation will be performed.

func recommend(userID int) []int {

// 先从缓存中查询是否存在已经计算好的推荐结果
cache, ok := userCache.Load(userID)
if ok {
    userCache := cache.(UserCache)
    // 如果缓存已经过期，则将该缓存清除
    if userCache.ExpiredTime.Before(time.Now()) {
        userCache.Delete(userID)
    } else {
        userCache.LastUsedTime = time.Now()  // 更新缓存的使用时间
        return userCache.Data
    }
}
// 如果缓存中不存在该用户的推荐结果，则进行实时计算
res := calRecommend(userID)
cacheTime := 10*time.Minute  // 缓存时间为10分钟
setUserCache(userID, res, cacheTime)   // 缓存推荐结果
return res

}

4. Summary

Through the above examples, we can see that in e-commerce In the recommendation algorithm, the caching mechanism is very necessary. It can improve recommendation efficiency while ensuring high accuracy and real-time performance of recommendation results. This article takes Golang as an example to introduce how to implement an efficient caching mechanism for e-commerce recommendation algorithms. In actual applications, the most appropriate cache strategy and granularity need to be selected based on the actual situation.

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