Paper Info
Title
A translation model for matching reviews to objects
Abstract
We develop a generic method for the review matching problem, which is to match unstructured text reviews to a list of objects, where each object has a set of attributes. To this end, we propose a translation model for generating reviews from a structured description of objects. We develop an EM-based method to estimate the model parameters and use this model to find, given a review, the object most likely to be the topic of the review. We conduct extensive experiments on two large-scale datasets: a collection of restaurant reviews from Yelp and a collection of movie reviews from IMDb. The experiments show that our translation model-based method is superior to traditional tf-idf based methods as well as a recent mixture model-based method for the review matching problem.
Year
DOI
Venue
2009
10.1145/1645953.1645977
CIKM
Keywords
Field
DocType
mixture model,language model
Data mining,Information retrieval,Computer science,Mixture model,Language model
Conference
Citations 
PageRank 
References 
7
0.59
26
Authors
4
Name
Order
Citations
PageRank
Nilesh Dalvi1176767.10
Ravi Kumar2139321642.48
Bo Pang35795451.00
Andrew Tomkins493881401.23