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We perform recommendations for the Social Ridesharing scenario, in which a set of commuters, connected through a social network, arrange one-time rides at short notice. In particu...
Search algorithms in image retrieval tend to focus on giving the user more and more similar images based on queries that the user has to explicitly formulate. Implicitly, such sys...
Sayantan Hore, Dorota Glowacka, Ilkka Kosunen, Kum...
The amount of available geo-referenced data has seen a dramatic explosion over the past few years. Human activities now generate digital traces that are annotated with location da...
Context-Aware Recommender System (CARS) models are trained on datasets of context-dependent user preferences (ratings and context information). Since the number of context-depende...
In social recomendation systems, users often publicly rate objects such as photos, news articles or consumer products. When they appear in aggregate, these ratings carry social si...
In this paper we compare several techniques to automatically feed a graph-based recommender system with features extracted from the Linked Open Data (LOD) cloud. Specifically, we...
Cataldo Musto, Pierpaolo Basile, Marco de Gemmis, ...
This work presents an empirical comparison among three widespread word embedding techniques as Latent Semantic Indexing, Random Indexing and the more recent Word2Vec. Specificall...
Cataldo Musto, Giovanni Semeraro, Marco de Gemmis,...
In implicit feedback datasets, non-interaction of a user with an item does not necessarily indicate that an item is irrelevant for the user. Thus, evaluation measures computed on ...
We investigate how metrics that can be measured offline can be used to predict the online performance of recommender systems, thus avoiding costly A-B testing. In addition to accu...