Jianxun Lian


2020

pdf bib
MIND: A Large-scale Dataset for News Recommendation
Fangzhao Wu | Ying Qiao | Jiun-Hung Chen | Chuhan Wu | Tao Qi | Jianxun Lian | Danyang Liu | Xing Xie | Jianfeng Gao | Winnie Wu | Ming Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

News recommendation is an important technique for personalized news service. Compared with product and movie recommendations which have been comprehensively studied, the research on news recommendation is much more limited, mainly due to the lack of a high-quality benchmark dataset. In this paper, we present a large-scale dataset named MIND for news recommendation. Constructed from the user click logs of Microsoft News, MIND contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body. We demonstrate MIND a good testbed for news recommendation through a comparative study of several state-of-the-art news recommendation methods which are originally developed on different proprietary datasets. Our results show the performance of news recommendation highly relies on the quality of news content understanding and user interest modeling. Many natural language processing techniques such as effective text representation methods and pre-trained language models can effectively improve the performance of news recommendation. The MIND dataset will be available at https://msnews.github.io.

pdf bib
PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision
Chuhan Wu | Fangzhao Wu | Tao Qi | Jianxun Lian | Yongfeng Huang | Xing Xie
Findings of the Association for Computational Linguistics: EMNLP 2020

User modeling is critical for many personalized web services. Many existing methods model users based on their behaviors and the labeled data of target tasks. However, these methods cannot exploit useful information in unlabeled user behavior data, and their performance may be not optimal when labeled data is scarce. Motivated by pre-trained language models which are pre-trained on large-scale unlabeled corpus to empower many downstream tasks, in this paper we propose to pre-train user models from large-scale unlabeled user behaviors data. We propose two self-supervision tasks for user model pre-training. The first one is masked behavior prediction, which can model the relatedness between historical behaviors. The second one is next K behavior prediction, which can model the relatedness between past and future behaviors. The pre-trained user models are finetuned in downstream tasks to learn task-specific user representations. Experimental results on two real-world datasets validate the effectiveness of our proposed user model pre-training method.