Diversified Query Generation Guided with Knowledge Graph

An overview of KeDy Model.


Relevant articles recommendation plays an important role in online news platforms. Directly displaying recalled articles by a search engine lacks a deep understanding of the article contents. Generating clickable queries, on the other hand, summarizes an article in various aspects, which can be henceforth utilized to better connect relevant articles. Most existing approaches for generating article queries, however, do not consider the diversity of queries or whether they are appealing enough, which are essential for boosting user experience and platform drainage. To this end, we propose a Knowledge-Enhanced Diversified QuerY Generator (KeDy), which leverages an external knowledge graph (KG) as guidance. We diversify the query generation with the information of semantic neighbors of the entities in articles. We further constrain the diversification process with entity popularity knowledge to build appealing queries that users may be more interested in. The information within KG is propagated towards more popular entities with popularity-guided graph attention. We collect a news-query dataset from the search logs of a real-world search engine. Extensive experiments demonstrate our proposed KeDy can generate more diversified and insightful related queries than several strong baselines.

In The 15th ACM International Conference on Web Search and Data Mining (WSDM 2022)
Jiangjie Chen
Jiangjie Chen
Ph.D. Candidate (on the job market!)

His research interests mainly include natural language reasoning and large language models.