Similarity measurement between two musical pieces is a hard problem. Humans perceive such similarity by employing a large amount of contextually semantic information. Commonly used content-based methodologies rely on information that includes little or no semantic information, and thus are reaching a performance “upper bound”. Recent research pertaining to contextual information assigned as fre...

The similarity and dissimilarity is a corresponding relationship which is the base of cognitive judgments. The main purpose of this paper is to study the user perception by using similarity judgments. In this study, fifteen innovative products are used as the stimuli which divided into three groups: global, creative and local products. A total of 139 student volunteers participated in the vario...

Abstract With the rapid development of Internet, recommendation systems have received widespread attention as an effective way to solve information overload. Social tagging technology can both reflect users’ interests and describe characteristics items themselves, making group thus becoming a in urgent demand nowadays. In traditional tag-based systems, general processing method is calculate sim...

This research aims to examine the psychology and behavior of users when customizing avatars from standpoint user experience provide constructive contributions Metaverse avatar customization platform. study analyzed factors that affect user-customized in different virtual environments compared differences public self-consciousness, self-expression, emotional expression among customized multiple ...

Understanding user listening behaviors is important to the personalization of music recommendation. In this paper, we present an approach that discovers user behavior from a large-scale, real-world listening record. The proposed approach generates a latent representation of users, listening sessions, and songs, where each of these objects is represented as a point in the multi-dimensional laten...

Neighbourhood-based collaborative filtering is one of the most popular recommendation techniques, and has been applied successfully in various fields. User ratings are often used by neighbourhood-based collaborative filtering to compute the similarity between two users or items, but, user ratings may not always be representatives of their true preferences, resulting in unreliable similarity inf...

Collaborative filtering (CF) is a powerful recommender system that generates a list of recommended items for an active user based on the ratings of similar users. This paper presents a novel approach to CF by first finding the set of users similar to the active user by adopting self-organizing maps (SOM), followed by k-means clustering. Then, the ratings for each item in the cluster closest to ...