=Paper=
{{Paper
|id=Vol-1673/invited1
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1673/invited1.pdf
|volume=Vol-1673
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1673/invited1.pdf</pdf>
<pre>
                From Reviews to Recommendations
                                         Barry Smyth
                                   University College Dublin
                                     barry.smyth@ucd.ie




                                           Abstract

Recommender systems are now a familiar part of the digital landscape helping us to choose
which movies to watch and books to read. They guide us about where to stay and eat when we
travel. They help us to keep in touch with friends and may even influence our choice of a mate.
To do this recommender systems require data. Lots of data.

In the early years this data came in the form of our online transactions and item ratings. More
recently recommendations have been influenced by our social networks, the connections that
link us, and the things that we share with others. Today there is a new form of data that has the
potential to drive recommender systems of the future: user-generated reviews. Reviews are now
a routine part of how we make decisions, large and small. Most of us wouldn’t dream of
booking a hotel without first checking out its reviews and companies like TripAdvisor and Yelp
have build billion dollar enterprises on the opinions of millions of people.

In this talk we will discuss the role of user generated reviews in a new generation of
recommender systems and some of the ways that opinions can be leveraged to better
understand users and generate new forms of recommendations. We will focus on how opinion
mining techniques can be used to extract features and sentiment from unstructured review text
and ways to use this information in recommendation ranking and explanation.




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