Paper Info
Title
Addressing Complex and Subjective Product-Related Queries with Customer Reviews
Abstract
Online reviews are often our first port of call when considering products and purchases online. When evaluating a potential purchase, we may have a specific query in mind, e.g. `will this baby seat fit in the overhead compartment of a 747?' or `will I like this album if I liked Taylor Swift's 1989?'. To answer such questions we must either wade through huge volumes of consumer reviews hoping to find one that is relevant, or otherwise pose our question directly to the community via a Q/A system. In this paper we hope to fuse these two paradigms: given a large volume of previously answered queries about products, we hope to automatically learn whether a review of a product is relevant to a given query. We formulate this as a machine learning problem using a mixture-of-experts-type framework---here each review is an `expert' that gets to vote on the response to a particular query; simultaneously we learn a relevance function such that `relevant' reviews are those that vote correctly. At test time this learned relevance function allows us to surface reviews that are relevant to new queries on-demand. We evaluate our system, Moqa, on a novel corpus of 1.4 million questions (and answers) and 13 million reviews. We show quantitatively that it is effective at addressing both binary and open-ended queries, and qualitatively that it surfaces reviews that human evaluators consider to be relevant.
Year
DOI
Venue
2015
10.1145/2872427.2883044
Proceedings of the 25th International Conference on World Wide Web
Keywords
Field
DocType
Relevance ranking, question answering, text modeling, reviews, bilinear models
Data science,Data mining,Text modeling,World Wide Web,Question answering,Swift,Computer science,Customer reviews,Artificial intelligence,Machine learning
Journal
Volume
Citations 
PageRank 
abs/1512.06863
25
0.81
References 
Authors
36
2
Name
Order
Citations
PageRank
Julian John McAuley12856115.30
alex yang2250.81