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CORR
2010
Springer

Recommender Systems by means of Information Retrieval

9 years 1 months ago
Recommender Systems by means of Information Retrieval
In this paper we present a method for reformulating the Recommender Systems problem in an Information Retrieval one. In our tests we have a dataset of users who give ratings for some movies; we hide some values from the dataset, and we try to predict them again using its remaining portion (the so-called "leave-n-out approach"). In order to use an Information Retrieval algorithm, we reformulate this Recommender Systems problem in this way: a user corresponds to a document, a movie corresponds to a term, the active user (whose rating we want to predict) plays the role of the query, and the ratings are used as weigths, in place of the weighting schema of the original IR algorithm. The output is the ranking list of the documents (" users") relevant for the query ("active user"). We use the ratings of these users, weighted according to the rank, to predict the rating of the active user. We carry out the comparison by means of a typical metric, namely the accur...
Alberto Costa, Fabio Roda
Added 29 May 2011
Updated 29 May 2011
Type Journal
Year 2010
Where CORR
Authors Alberto Costa, Fabio Roda
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