activity
20092017
most citedProbabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments

443 citations · 521 across the 7 of their papers we have counts for

collaborators

7 papers

stat.ML20172 cited

Tree-Structured Boosting: Connections Between Gradient Boosted Stumps and Full Decision Trees

José Marcio Luna, Eric Eaton, Lyle H. Ungar +7

Additive models, such as produced by gradient boosting, and full interaction models, such as classification and regression trees (CART), are widely used algorithms that have been i…

cs.CL201719 cited

Domain Aware Neural Dialog System

Sajal Choudhary, Prerna Srivastava, Lyle Ungar +1

We investigate the task of building a domain aware chat system which generates intelligent responses in a conversation comprising of different domains. The domain, in this case, is…

cs.CL20177 cited

Enterprise to Computer: Star Trek chatbot

Grishma Jena, Mansi Vashisht, Abheek Basu +2

Human interactions and human-computer interactions are strongly influenced by style as well as content. Adding a persona to a chatbot makes it more human-like and contributes to a…

cs.CL2017

Deriving Verb Predicates By Clustering Verbs with Arguments

Joao Sedoc, Derry Wijaya, Masoud Rouhizadeh +2

Hand-built verb clusters such as the widely used Levin classes (Levin, 1993) have proved useful, but have limited coverage. Verb classes automatically induced from corpus data such…

cs.IR2013443 cited

Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments

Alexandrin Popescul, Lyle H. Ungar, David M Pennock +1

Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, a…

cs.CL201249 cited

Two Step CCA: A new spectral method for estimating vector models of words

Paramveer Dhillon, Jordan Rodu, Dean Foster +1

Unlabeled data is often used to learn representations which can be used to supplement baseline features in a supervised learner. For example, for text applications where the words…