3,036 research outputs found
The Hellinger Correlation
In this paper, the defining properties of a valid measure of the dependence
between two random variables are reviewed and complemented with two original
ones, shown to be more fundamental than other usual postulates. While other
popular choices are proved to violate some of these requirements, a class of
dependence measures satisfying all of them is identified. One particular
measure, that we call the Hellinger correlation, appears as a natural choice
within that class due to both its theoretical and intuitive appeal. A simple
and efficient nonparametric estimator for that quantity is proposed. Synthetic
and real-data examples finally illustrate the descriptive ability of the
measure, which can also be used as test statistic for exact independence
testing
Toward Word Embedding for Personalized Information Retrieval
This paper presents preliminary works on using Word Embedding (word2vec) for
query expansion in the context of Personalized Information Retrieval.
Traditionally, word embeddings are learned on a general corpus, like Wikipedia.
In this work we try to personalize the word embeddings learning, by achieving
the learning on the user's profile. The word embeddings are then in the same
context than the user interests. Our proposal is evaluated on the CLEF Social
Book Search 2016 collection. The results obtained show that some efforts should
be made in the way to apply Word Embedding in the context of Personalized
Information Retrieval
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