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82
Voted
RECSYS
2015
ACM
9 years 11 months ago
Parsimonious and Adaptive Contextual Information Acquisition in Recommender Systems
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...
Matthias Braunhofer, Ignacio Fernández-Tob&...
78
Voted
RECSYS
2015
ACM
9 years 11 months ago
Asymmetric Recommendations: The Interacting Effects of Social Ratings? Direction and Strength on Users' Ratings
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...
Oded Nov, Ofer Arazy
72
Voted
RECSYS
2015
ACM
9 years 11 months ago
Automatic Selection of Linked Open Data Features in Graph-based Recommender Systems
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, ...
130
Voted
RECSYS
2015
ACM
9 years 11 months ago
Word Embedding Techniques for Content-based Recommender Systems: An Empirical Evaluation
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,...
84
Voted
RECSYS
2015
ACM
9 years 11 months ago
Top-N Recommendation with Missing Implicit Feedback
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 ...
Daryl Lim, Julian McAuley, Gert R. G. Lanckriet