# 推荐算法介绍

## 1. 推荐算法知识架构

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There are many kinds of recommendation algorithms, which can be divided into the following categories:

## 2. 协同过滤推荐算法（Collaborative Filter，CF）

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Because this recommendation algorithm can get better recommendation effect through statistics-based machine learning algorithm, and it is easy to implement in engineering, so the vast majority of recommendation algorithms are CF. CF can be implemented in the following ways:

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Generally speaking, if the number of item is not large, for example, no more than 100, 000, and does not increase significantly, use item-base. Because when the number of item is small and does not increase significantly, it shows that the relationship between item is relatively stable over a period of time (compared with the relationship between user), the need for real-time updating of item-similarity reduces a lot of recommendation systems and improves the efficiency, so item-base is better. On the contrary, when the number of item is large, it is recommended to use user-base.

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As a classical recommendation algorithm, collaborative filtering is widely used in industry. It has many advantages, strong versatility of the model, does not need much professional knowledge in the corresponding data field, simple engineering implementation, and good results. These are the reasons why it is popular.

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Of course, collaborative filtering also has some unavoidable problems, such as the headache of “cold start”. When we don’t have any data for new users, we can’t recommend items for new users. At the same time, it does not take into account the differences in scenarios, such as based on the user’s scenario and the user’s current mood. Of course, you can’t get some minority’s unique preferences, which is good at content-based recommendations.

## 3. 基于内容的推荐算法（Content-based Filter，CB）

CB的思想是这样的：根据用户在过去喜欢的内容，为用户推荐与其过去喜欢内容相似的内容。CB的关键在于内容相似性的度量，这是CB在运用过程中的核心。CB的过程一般包括以下三步：

CF和CB都有自己的局限性。目前，大多数推荐系统都是基于CB(如CF)以外的算法，以CB为辅助，形成混合式推荐系统。

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Both CF and CB have their own limitations. At present, most recommendation systems are based on algorithms other than CB (such as CF), with CB as the auxiliary to form a hybrid recommendation system.

## 4.基于人口统计信息的推荐算法（Demographic-based，DB）

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The recommendation algorithm based on demography should be the easiest to implement. Because it only uses the basic information of the user, such as age, gender, etc., to measure the similarity of the user, and then recommends the items of other user preferences similar to the user to the current user.

## 5.混合推荐算法（Hybrid Recommender，HR）

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The common problem of CF, CB, DB and other recommendation algorithms mentioned above is that they have both advantages and disadvantages. In order to get a better recommendation algorithm, it is a natural idea to combine many recommendation algorithms as a whole. After the fusion of a variety of recommendation algorithms, HR will not be worse than any single recommendation algorithm in theory, but the complexity of HR will also increase accordingly, so in practical use, the use of HR for recommendation is not as common as CF.

Original: https://blog.csdn.net/u010451780/article/details/109495597
Author: jack_201316888
Title: 推荐算法介绍

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