M. Degemmis

Publications197
h-index30
Citations4,581
Highly Influential Citations222
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Content-based Recommender Systems: State of the Art and Trends
TLDR
The role of User Generated Content is described as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.
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Introducing Serendipity in a Content-Based Recommender System
TLDR
This paper presents the design and implementation of a hybrid recommender system that joins a content-based approach and serendipitous heuristics in order to mitigate the over-specialization problem with surprising suggestions.
View on IEEE
AlBERTo: Italian BERT Language Understanding Model for NLP Challenging Tasks Based on Tweets
TLDR
A BERT language understanding model for the Italian language (AlBERTo) is trained, focused on the language used in social networks, specifically on Twitter, obtaining state of the art results in subjectivity, polarity and irony detection on Italian tweets.
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A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation
TLDR
This work proposes a new content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles, in which user preferences are stored, instead of comparing their rating styles.
View on Springer
Learning Word Embeddings from Wikipedia for Content-Based Recommender Systems
TLDR
This paper compared the effectiveness of three widespread approaches as Latent Semantic Indexing, Random Indexing and Word2Vec in the task of learning a vector space representation of both items to be recommended as well as user profiles.
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An investigation on the serendipity problem in recommender systems
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Ontologically-Enriched Unified User Modeling for Cross-System Personalization
TLDR
This paper proposes the use of a common ontology-based user context model as a basis for the exchange of user profiles between multiple systems and, thus, as a foundation for cross-system personalization.
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Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data
TLDR
A graph-based algorithm leveraging LOD-based features is able to overcome several state of the art baselines, such as collaborative filtering and matrix factorization, thus confirming the effectiveness of the proposed approach.
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A Framework for the Development of Personalized Agents
TLDR
This paper presents a general framework designed according to the idea of dialoguing agents that exploit the knowledge stored in user profiles in order to develop intelligent e-business applications.
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Human Decision Making and Recommender Systems
TLDR
How the coupling of recommendation algorithms with the understanding of human choice and decision making theory has the potential to benefit research and practice on recommender systems and to enable users to achieve a good balance between decision accuracy and decision effort is highlighted.
View on ACM
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