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...

Now, when, according to recent Web evolution trends, a human becomes a very dynamic and proactive player in a large highly heterogeneous and distributed environment with a huge amount of different kind of data, services, devices, etc., it is quite necessary to provide a technology and tools for easy and handy human information access and manipulation. Context-awareness and intelligence of user ...

Using data collected across 19 years, the chief goal of this study was to discover predictors of continued relational closeness between best friends. Participants were same-sex and cross-sex best friend pairs recruited from a small Midwestern college. In 1983, participants completed several tests and activities designed to assess facets of intimacy, with follow-up studies in 1987 and 2002 measu...

A series of field studies focused on the role of similarity as niche construction in friendships. Using a free-range dyad harvest method, we collected 11 independent samples with 1,523 interacting pairs, and compared dyad members' personality traits, attitudes, values, recreational activities, and alcohol and drug use. Within-dyad similarity was statistically significant on 86% of variables mea...

This study examined connections between pitch-class set-theoretical abstract concepts, set-classes, and perceptual estimations of chords derived from the set-classes. The study had two aims, the first of which was to compare theoretical resemblance with perceived closeness. Another aim was to illuminate and analyze both factors relevant for perceptual estimations of chords and factors relevant ...

This article reviews the problem of degree of closeness and interaction level in a social network by ranking users based on similarity score. This similarity is measured on the basis of social, geographic, educational, professional, shared interests, pages liked, mutual interested groups or communities and mutual friends. The technique addresses the problem of matching user profiles in its glob...

Drawing upon the methodology developed by Kenny and Acitelli (2001), this study examined empathic accuracy and assumed similarity in both parties' perceptions of coach-athlete relationships. One hundred and twenty-one coaches and athletes reported on their direct-perceptions and meta-perceptions of closeness, commitment and complementarity (3 Cs; Jowett & Cockerill, 2002), and of satisfaction w...

Clustering is one of the very powerful and widely used technique in information retrieval. All clustering methods works on finding relationship among data objects. There are various similarity measures used along with criterion functions to find similarity between documents like cosine, jaccard etc. Clustering efficiency and performance is highly dependent on the accuracy of the similarity meas...

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...