Akira Tajima

Quantifying Query Ambiguity with Topic Distributions

2016

Query ambiguity is a useful metric for search engines to understand users' intents. Existing methods quantify query ambiguity by calculating an entropy of clicks. These methods assign each click to a one-hot vector corresponding to …

Embedding-based News Recommendation for Millions of Users

2017

It is necessary to understand the content of articles and user preferences to make effective news recommendations. While ID-based methods, such as collaborative filtering and low-rank factorization, are well known for making recommendations, they are …