Song recommendation system combined with social network Dong Zheng

Nowadays, personalized recommendations are becoming increasingly popular. They recommend information and products that interest users based on their interests and purchasing behaviors.

Planned product features:

We plan to launch an online singing system that combines the user's social network information and the user's history record to recommend the songs he may like to sing.

 

Product Analysis:

1. Demand Analysis: As the scale of E-commerce continues to expand, the number and type of goods are growing rapidly. It takes a lot of time for customers to find the goods they want to buy. This kind of browsing of a large amount of irrelevant information and product processes will undoubtedly cause the constant loss of consumers in the information overload problem. The personalized recommendation system can solve these problems.

 

2. Method: Use Social Network Information for collaborative filtering and recommendation.

You can perform item-based collaborative filtering by using your historical record.

The two methods are used in combination for recommendation.

 

3. Benefits: it can save the user's search time and help the user discover the songs he doesn't realize, that is, promote consumption.

 

4. Competition: some of the current song recommendation systems only recommend popular songs, while others simply use the historical consumption records of users.

In this software, we can add appropriate penalty factors to popular songs, and add social network information recommendations based on user history consumption records, we can predict that the accuracy and diversity of Recommendation results will be improved.

Song recommendation system combined with social network Dong Zheng