With the development of science and technology, many people find that the short video system seems to have a kind of magical magic, as if they can read people’s minds. The recommended short video content is more and more in line with personal preferences. This has nothing to do with magic. It’s just a variety of recommendation algorithms used in short video system development.

I. Content-based recommendation algorithm

The content-based recommendation algorithm developed by the short video system is to summarize the preferences and characteristics of users according to the historical data of the short video content through machine learning method, and then realize the prediction of the short video content that users are interested in. This algorithm will change according to the change of users’ preferences.

Advantages: It can well realize the modeling of user interest, and improve the accuracy of recommendation algorithm for short video system development through the increase of different types of short video content.

Disadvantages: The attributes of short video content are limited, and the learning data obtained is limited; The similarity of short video content is very high, and the summary data is one-sided to a certain extent. In short video system development, the recommendation algorithm needs historical data to provide the basis, and there is a cold start problem.

2. Recommendation algorithm based on collaborative filtering

The realization of the collaborative filtering recommendation algorithm developed by the short video system is to predict the short video content that the target user may be interested in by taking advantage of the preferences of the neighboring users to the short video content. The short video system will recommend the short video content to the target user according to the predicted results.

Advantages:

1. In the case of insufficient basic data in the early stage of short video system development, recommendation algorithm can also be realized by sharing experience of others.

2. The recommendation algorithm based on collaborative filtering finds the potential interest preferences of target users, and the short video system can recommend more dissimilar short video contents that are likely to be liked by users.

3. During the development of short video system, feedback information from other similar users can be effectively used to speed up personalized learning of recommendation algorithm.

Disadvantages: It is difficult for new users to obtain data support because there are no neighboring users in the early stage of using the short video system, and the recommendation accuracy is not high in the early stage.

Combination recommendation algorithm

Because all kinds of algorithms have their advantages and disadvantages, different recommendation algorithms will be combined in the development of short video system. Although there are many different combination methods such as weighting, transformation, mixing and cascade in theory, in practical application, as long as the technical weaknesses of different recommendation algorithms can be avoided and made up for.

Although short video system development is make full use of the fragmentation of the time, but the cycle of short video content playback, for a short video content under unknown curiosity will lead to the user the passage of time are silent when brush short video, this is probably the charm of short video system, short video system development should be people-oriented, make full use of technical means to optimize the user experience.

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