Chun Zeng, Jiangjie Chen, Tianyi Zhuang, Rui Xu, Hao Yang, Qin Ying, Shimin Tao, Yanghua Xiao. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

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.

Collaborative filtering has been very successful in both research and applications such as information filtering and E-commerce. The k-Nearest Neighbor (KNN) method is a popular way for its realization. Its key technique is to find k nearest neighbors for a given user to predict his interests. However, this method suffers from two fundamental problems: sparsity and scalability. In this paper, we present our solutions for these two problems. We adopt two techniques: a matrix conversion method for similarity measure and an instance selection method. And then we present an improved collaborative filtering algorithm based on these two methods. In contrast with existing collaborative algorithms, our method shows its satisfactory accuracy and performance.