4 citations · 6 across the 4 of their papers we have counts for
4 papers · 1 filter
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion
Adam Block, Rahul Kidambi, Daniel N. Hill +2
Conventional methods for query autocompletion aim to predict which completed query a user will select from a list. A shortcoming of this approach is that users often do not know wh…
Session-Aware Query Auto-completion using Extreme Multi-label Ranking
Nishant Yadav, Rajat Sen, Daniel N. Hill +2
Query auto-completion (QAC) is a fundamental feature in search engines where the task is to suggest plausible completions of a prefix typed in the search bar. Previous queries in t…
A Zero Attention Model for Personalized Product Search
Qingyao Ai, Daniel N. Hill, S. V. N. Vishwanathan +1
Product search is one of the most popular methods for people to discover and purchase products on e-commerce websites. Because personal preferences often have an important influenc…
Adaptive, Personalized Diversity for Visual Discovery
Choon Hui Teo, Houssam Nassif, Daniel Hill +4
Search queries are appropriate when users have explicit intent, but they perform poorly when the intent is difficult to express or if the user is simply looking to be inspired. Vis…