TFM 2016/17 Exploiting subsequence matching in Recommender Systems


Pablo Sanchez Perez

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Exploiting subsequence matching in Recommender Systems


Recommender Systems (RS) are software tools that allow users nding the items and
information they need in a simple and direct way. These items are related to the specic
domain of the recommender system, although movies, books, and music are some of the
most studied domains in the scientic community. In order to predict the most interesting
items for each user in the system, these methods analyze the tastes and interests of the
users to make personalized recommendations [36].
Although the rise of the Internet dates back to the early 1970s, the research in these
systems has taken place especially in the last 20 years, due to the global spread of information
and communication technologies. The antecedents of these systems can be found
in the early 1990s, in the Tapestry [20] and Grouplens [35] projects, co-occurring with
the rise of the Internet. However, they are especially relevant today as they have now
become essential to lter the large amount of data available in the cloud. A large number
of important companies oering online services make use of recommendation algorithms
to expand their economic activity and improve the user experience. Some examples are
Amazon (online store), Youtube (videos) or Net ix (streaming audiovisual content). This
last company became very popular in 2006 for a three-year contest with a prize of 1 million
dollars to the research group who managed to improve its prediction algorithm by 10% [31].
The team \BellKor's Pragmatic Chaos" ended up winning this contest and putting these
technologies in the spotlight.


Exploiting subsequence matching in Recommender Systems
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noviembre 27, 2018

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