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...
Compact representation is a key issue for effective information delivery to users in mobile content-providing services. In particular, it is more severe when providing text docume...
Jung-Woo Ha, Dongyeop Kang, Hyuna Pyo, Jeonghee Ki...
Automated playlist generation is a special form of music recommendation and a common feature of digital music playing applications. A particular challenge of the task is that the ...
We introduce EasyEx, a recommendation-based book exchange system which identifies potential exchanges for a user solely based on the user’s item list. EasyEx is novel, since it...
This paper presents a context-aware mobile shopping recommender system. A critique-based baseline recommender system is enhanced by the integration of context conditions like weat...