Can the “recommendation system” fully understand your customers’ preferences?

1Personalized recommended apps

With the development of information technology and the Internet, people have gradually entered an era of information overload from an era of information scarcity.Search engines have solved the problem of information overload in the early days. However, as the amount of information continues to expand, it is even difficult for users to accurately provide keywords that search engines need. In order to avoid users wasting time screening too much junk information, recommendation systems have emerged.

At present, recommendation systems have been widely used in life. For example, the current Hong Kong 01 recommends personalized news to users.Recommended content（content-based recommender systems）；YouTube, Netflix, and Ðisney+ recommend videos, TV series, and movies to users, while Spotify, Apple Music, Joox, KKBOX, and MOOV recommend music that users likeCollaborative filtering（collaborative filtering recommender systems）;Taobao and Amazon recommend books, food, clothes and other products by combining different types of recommendation algorithms. This is called hybrid recommendation（hybrid recommender system）。

In addition, in social networks such as Twitter, Facebook, and Sina Weibo, link prediction and social network analysis are used to recommend friends or people they may know to users. These are also branches of recommendation algorithms. However, due to the influence of recommendation systems on online search engines or social media, the search results are incomplete and may be biased, which is not comprehensive enough for those who hope to understand the preferences, behaviors, ideas, and values ​​of target objects and customers through online data.

2 Introduction to mainstream recommendation system algorithms

Content-based recommendations

Content-based recommendations（content-based recommender systems，）It is the earliest and most mainstream recommendation method. To this day, there are still a large number of industrial recommendation system algorithms that integrate content recommendation algorithms. Content-based recommendation systems recommend andproducts similar to those he liked in the past.

The process of content-based recommendation system一It generally includes the following three steps: ①Item Representation：Extract features for the content of each item and use the extracted features to represent the item; ② User Profile Learning：Using the feature data of items a user liked (and disliked) in the past to learn the user's preference profile; ③ Recommendation Generation：Compare the user's profile with the features of the candidate items and recommend a set of most relevant items to the user.

Collaborative filtering recommendation

Since content-based recommendations are time-consuming and laborious,Collaborative filtering(collaborative filtering recommender systems，CF)Recommendation system, collaborative filtering recommendation system does not need to extract features, but only needs to providePurchase history and reviewsThe recommendation is completed. Collaborative filtering recommendation is divided intouser-based CFanditem-based CFThe ideas of these two are similar. User-based recommendations are when we need to recommend items to a user whois most similar to himJust recommend items that have been purchased but not purchased by this user to him. That is, making recommendations based on user behavior. Two areas of collaborative filtering recommendation includeLatent Factor Model Recommendation (also known as matrix factorization recommender systems) andEnsemble learning recommendationsystems，For a detailed explanation, see: https://mp.weixin.qq.com/s/K0rh6u0flt_gadQj3n4IPw 。

3 Summary

This is the end of the introduction to mainstream recommendation system algorithms. In fact, the recommendation system algorithm is only one link in the industrial recommendation system. It also needs a lot of conventional recommendation methods to play a role. For example, the recommendation system algorithm hascold-startsame problem，cOld-start refers to when a new user or new item without any record enters our system, how do we make recommendations? We should first try to recommend him some new works that everyone likes, and then use the model to complete the recommendation when his past records are sufficient to indicate his preferences. However, if one wants to analyze the voices on the Internet fairly and comprehensively, the results on search engines or social media alone cannot give one a comprehensive understanding of the market, because the recommendation system algorithm adjusts the displayed results based on the user's own preferences or habits. In this case, social data analysis tools can come in handy.

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