- Strengthened security and privacy of users via WeChat authorized login interface
- Gave full consideration on comfort and feasibility of human-computer interaction regarding exploitation on the separation of logic and rendering.
- Enhanced user experiences concerning movie and article recommendation features.
- Unitized the obtained other document format into csv format for database processing with the help of Python String Library.
- Recommended articles and stored the title in Json document for personalization with the help of Python crawler technology.
- Utilized _id and _openid to operate the cloud database on the applet front end and the server side through the API.
- Utilized getWXContext method provided by wx-server-sdk to obtain the openid in the calling cloud function.
- API was responsible for file management both in the applet front end and the cloud function side.
- Randomly generated word id within a certain range.
- Accessed the cloud database query id information and performed string processing to return to the user interface.
- Formed a table in the iCloud database for storage upon clicking on the favorite button.
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Extended the connection between API interface and Scallop Word webpage to receive the pronunciation and explain the example sentence.
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Accessed the cloud database query id information and performed string processing to return to the user interface.
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Established a sync await to asynchronously load preferred articles and record articles of users.
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Formalized artificial personal recommendation based on historic data of users.
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Achieved the functions of similar hobby users regarding User Collaborative Filtering algorithm.
Calculate the similarity between two users through the Jaccard formula.
Let N (u) be the set of items that user u likes, and N (v) be the set of items that user v likes.
Then the similarity of u and v is calculated by the following formula:
w_un=|N(u)∩N(v)|/|N(u)∪N(v)|
