The coefficient of correlation evaluates the similarity of two sets of measurements (i.e., two dependent variables) obtained on the sameobservations. The coefficient of correlation indicates the amount of information common to two variables. This coefficient takes values between −1 and +1 (inclusive). A value of 0 indicates that the two series of measurement have nothing in common. A value of +...

Recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems. In order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem a...

This study proposes a novel method to extract the configuration of the psychological space by directly measuring subjects' similarity rating without computational work. Although multidimensional scaling (MDS) is well-known as a conventional method for extracting the psychological space, the method requires many pairwise evaluations. The times taken for evaluations increase in proportion to the ...

Semantic similarity is a central concept that extends across numerous fields such as artificial intelligence, natural language processing, cognitive science and psychology. Accurate measurement of semantic similarity between words is essential for various tasks such as, document clustering, information retrieval, and synonym extraction. We propose a novel model of semantic similarity using the ...

In wide area networks, retrieving the relevant text is a challenging task for information retrieval because most of the information requests are text based. The focus of paper is on the similarity measurement, performance evaluation and design of information retrieval techniques using the four similarity functions i.e. Jaccard, Cosine, Dice and Overlap. The performance evaluation of these simil...

Recommender systems and search engines are examples of systems that have used techniques such as Pearson’s product-momentum correlation coefficient or Cosine similarity for measuring semantic similarity between two entities. These methods relinquish semantic relations between pairs of features in the vector representation of an entity. This paper describes a new technique for calculating semant...

Recommender systems and search engines are examples of systems that have used techniques such as Pearson’s product-momentum correlation coefficient or Cosine similarity for measuring semantic similarity between two entities. These methods relinquish semantic relations between pairs of features in the vector representation of an entity. This paper describes a new technique for calculating semant...

Recommender systems and search engines are examples of systems that have used techniques such as Pearson’s product-momentum correlation coefficient or Cosine similarity for measuring semantic similarity between two entities. These methods relinquish semantic relations between pairs of features in the vector representation of an entity. This paper describes a new technique for calculating semant...

It is challenging to find a small set of data points, so-called exemplars or landmarks, that are nicely representative of itself and other data points. Affinity propagation (AP) is an effective algorithm that identifies exemplars among data points by recursively sending realvalued messages between pairs of data points. AP calculates the message using the similarity among data points. Hence, the...

Comparing and quantifying corpora is a key issue in corpus based translation and corpus linguistics, for which there is still a notable lack of measures. This makes it difficult for a user to isolate, transpose, or extend the interesting features of a corpus to other NLP systems. In this work we address the issue of measuring similarity between corpora. We suggest a scale between two user chose...