4 citations · 4 across the 2 of their papers we have counts for
6 papers
Toward Understanding Privileged Features Distillation in Learning-to-Rank
Shuo Yang, Sujay Sanghavi, Holakou Rahmanian +2
In learning-to-rank problems, a privileged feature is one that is available during model training, but not available at test time. Such features naturally arise in merchandised rec…
Embracing Structure in Data for Billion-Scale Semantic Product Search
Vihan Lakshman, Choon Hui Teo, Xiaowen Chu +4
We present principled approaches to train and deploy dyadic neural embedding models at the billion scale, focusing our investigation on the application of semantic product search.…
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…
An Efficient Bandit Algorithm for Realtime Multivariate Optimization
Daniel N Hill, Houssam Nassif, Yi Liu +2
Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Ofte…
Diversifying Music Recommendations
Houssam Nassif, Kemal Oral Cansizlar, Mitchell Goodman +1
We compare submodular and Jaccard methods to diversify Amazon Music recommendations. Submodularity significantly improves recommendation quality and user engagement. Unlike the Jac…
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…