这篇论文发表在2018年的WWW上。引入知识来进行新闻推荐。
关键词：News recommendation; knowledge graph representation; deep neural networks; attention model
Motivation
过去的新闻推荐方法没有引入知识，很难发现潜在的知识层面的关系
另一方面 新闻推荐有高度的时间敏感性并且要随着用户的兴趣而改变。
新闻推荐面临的三个挑战:
- highly time-sensitive and their relevance expires quickly within a short period,协同过滤算法效果不好
- How to dynamically measure auser’s interest based on his diversified reading history for current candidate news
- news language is usually highly condensed and comprised of a large amount of knowledge entities and common sense. 传统方法没有引入知识 而只是基于词语共现和聚类。
To extract deep logical connections among news, it is necessary to introduce additional knowledge graph information into news recommendations. 引入外部知识到新闻推荐中。
Methodology
我们把用户 i i i的点击历史表示为 [ t 1 i , t 2 i , t 2 i , . . . , t N i i ] [ t_{1}^{i},t_{2}^{i},t_{2}^{i},...,t_{N_{i}}^{i} ] [t1i,t2i,t2i,...,tNi