Studies show that the vast majority of queries to search engines are short and vague in specifying a user’s intent. Different users may have completely different information needs and goals when using precisely the same query. For example, User A is finding information about Apply Company by issuing a query "apple,", while User B is finding information related to fruit apple using the same query. When such a query is issued, search engines will return a list of documents that mix different topics. It takes time for a user to choose which information he/she wants.
Search Result Diversification is an effective way to solve this problem. It provides a list of results that cover as many aspects as possible, so that most users can be satisfied by the top results.
We work on:
- Multi-dimensional search result diversification
- Hierachical search result diversification (Details and Dataset )
- Search result diversification evaluation (Details and Dataset )
- Learning to Diversify Search Result via Subtopic Attention(Details and Dataset )
- GAN for Search Result Diversification (DVGAN)
- Search Result Diversification based on Self-Attention Network
- Search Result Diversification based on Intent Graph and GCN
Datasets