WeChat Search after View

Provides users with adaptive search query recommendations after reading an article or watching a video to encourage further in-depth learning.

My second project at WeChat was based on the search-as-learning theory in information science, to alleviate the information fragmentation problem in recommender systems. I devised a Search-after-View product that provides users with adaptive search query recommendations after reading an article or watching a video to encourage further in-depth learning.

Search after View

If one reads an article about Claude Monet’s Biography and Art, there will be search suggestions for “essential impressionist artists” or “Claude Monet’s palettes and techniques.” I data-mined user search logs, conducted semi-structured interviews, and worked closely with the algorithm team for a year to develop ideal strategies.

In this video (shown in Simplified Chinese), we read an article on new AI/cloud computing products in our Subsciptions. Then, when we go back to Search, we can see that at the bottom, the recommender system would recommend me relevant topics to search for. In this specific example, we get recommended topics like “new phones from Elon Musk” and “cloud technologies and applications”. Such recommendations encourage users to further explore the relevant topics to a greater extent.

More information

Download the App and try it out!.