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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. Expand
A Comparative Analysis of Methods for Pruning Decision Trees
TLDR
A comparative study of six well-known pruning methods with the aim of understanding their theoretical foundations, their computational complexity, and the strengths and weaknesses of their formulation, and an objective evaluation of the tendency to overprune/underprune observed in each method is made. Expand
An Enhanced Lesk Word Sense Disambiguation Algorithm through a Distributional Semantic Model
TLDR
A new Word Sense Disambiguation (WSD) algorithm which extends two well-known variations of the Lesk WSD method which relies on the use of a word similarity function defined on a distributional semantic space to compute the gloss-context overlap. Expand
Centroid-based Text Summarization through Compositionality of Word Embeddings
TLDR
This paper proposes a centroidbased method for text summarization that exploits the compositional capabilities of word embeddings and achieves good performance even in comparison to more complex deep learning models. Expand
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. Expand
Knowledge-Intensive Induction of Terminologies from Metadata
TLDR
A knowledge-intensive inductive approach to the induction and revision of terminologies from metadata, that can deal with on the expressive Semantic Web representations based on Description Logics, which are endowed with well-founded reasoning capabilities. Expand
Multistrategy Learning for Document Recognition
TLDR
A methodology for document classification and understanding is proposed, based on a multistrategy approach to learning from examples, which embeds two empirical learning systems: RES and INDUBIIH. Expand
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. Expand
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. Expand
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. Expand
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