Recommender Systems
| Autor | Rossana Ducato |
| Páginas | 257-261 |
257
Recommender Systems
74 Recommender Systems
Rossana Ducato
‘Recommender systems’ are algorithms aimed at supporting users
in their online decision making. More specifically, in the computer
science literature, a recommender system is defined as:
a specific type of advice-giving or decision support
system that guides users in a personalized way to
interesting or useful objects in a large space of possible
options or that produces such objects as output
(Felfernig et al., 2018).
Examples of such systems are the Amazon recommender tool for
products, the Netflix algorithm that suggests movies, the Facebook
software that finds ‘friends’ we might know.
A key element of recommender systems is that their suggestions are
personalized, i.e., based on users’ preferences. Such information can be
directly obtained from users (e.g., asking specifically for her preferences)
or can be generated by observation of their behavior (Jannach et al.,
2010). Most recommender systems rely on machine learning techniques,
including deep neural networks (Goanta, Spanakis, 2020).
From a technical point of view, four main models of recommendation
systems have been identified (Aggarwal 2016:1) collaborative filtering
systems; 2) content-based recommender systems; 3) knowledge-
based recommender systems; 4) hybrid systems.
Collaborative filtering systems perform the recommendation process
based on the user-item interaction provided by several users. Let
us assume A and B have similar tastes and that the algorithm has
recorded such a similarity. A rates the movie Titanic highly, the
recommender system infers that the rating of B for Titanic will be
likely to be similar. Hence, the algorithm formulates Titanic as a
recommendation for B.
Content-based recommender systems construct a predictive model
thanks to the attributes (descriptive features) of users or items.
Following in the movie example: A rated Titanic highly. Titanic is
described by keywords like “drama” and “love affair”. Therefore,
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