Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Automatic Information Organization and Retrieval.
Automatic Information Organization and Retrieval.
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
The million dollar programming prize
IEEE Spectrum
Collaborative filtering recommender systems
The adaptive web
An information retrieval model based on discrete fourier transform
IRFC'10 Proceedings of the First international Information Retrieval Facility conference on Adbances in Multidisciplinary Retrieval
Structured collaborative filtering
Proceedings of the 20th ACM international conference on Information and knowledge management
Enhanced Information Retrieval by Exploiting Recommender Techniques in Cluster-Based Link Analysis
Proceedings of the 2013 Conference on the Theory of Information Retrieval
Proceedings of the 2013 international workshop on Mining unstructured big data using natural language processing
I pinned it. where can i buy one like it?: automatically linking pinterest pins to online webshops
Proceedings of the 2013 workshop on Data-driven user behavioral modelling and mining from social media
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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 accuracy of the predictions returned by the algorithm, and we compare this to the real ratings from users. In our first tests, we use two different Information Retrieval algorithms: LSPR, a recently proposed model based on Discrete Fourier Transform, and a simple vector space model.