Accurately recommending candidate news articles to users has always been the core challenge of news recommendation system. News recommendations often require modeling of user interest to match candidate news. Recent efforts have primarily focused on extracting local subgraph information in a global click graph constructed by the clicked news sequence of all users. However, the computational complexity of extracting global click graph information has hindered the ability to utilize far-reaching linkage which is hidden between two distant nodes in global click graph collaboratively among similar users. To overcome the problem above, we propose a Global-view Long Chain Interests Modeling for news recommendation (GLoCIM), which combines neighbor interest with long chain interest distilled from a global click graph, leveraging the collaboration among similar users to enhance news recommendation. We therefore design a long chain selection algorithm and long chain interest encoder to obtain global-view long chain interest from the global click graph. We design a gated network to integrate long chain interest with neighbor interest to achieve the collaborative interest among similar users. Subsequently we aggregate it with local news category-enhanced representation to generate final user representation. Then candidate news representation can be formed to match user representation to achieve news recommendation. Experimental results on real-world datasets validate the effectiveness of our method to improve the performance of news recommendation.

本文利用知网(HowNet)中的知识,并将Word2vec模型的结构和思想迁移至义原表示学习过程中,提出了一个基于义原表示学习的词向量表示方法。首先,本文利用OpenHowNet获取义原知识库中的所有义原、所有中文词汇以及所有中文词汇和其对应的义原集合,作为实验的数据集。然后,基于Skip-gram模型,训练义原表示学习模型,进而获得词向量。最后,通过词相似度任务、词义消歧任务、词汇类比和观察最近邻义原,来评价本文提出的方法获取的词向量的效果。通过和基线模型比较,发现本文提出的方法既高效又准确,不依赖大规模语料也不需要复杂的网络结构和繁多的参数,也能提升各种自然语言处理任务的准确率。