Data Mining - Concepts and Techniques.

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

This paper regards bookmarking as the most important information to extract user preferences among user behaviors. Bookmarks are categorized on Bayesian networks by an ontology. Considering the relationships between categories, evidential supports are mutually propagated to improve the coverage of the potential preferences. Consequently, we have attempted to define bookmarking behaviors and apply them to the weight updating on users' preference map. We have measured the causal rate in order to improve accuracy of evidential supports and retrieved relational information between the behavioral patterns and user preferences throught temporally analyzing these patterns. For experiments, we made a dataset organized as 2718 bookmarks and had monitored 12 users' behaviors for 30 days.

Extracting user interests from bookmarks on the web

Traditional recommendation systems provide appropriate information to a target user after analyzing user preferences based on user profiles and rating histories. However, most of people also consider the friend's opinions when they purchase some products or watch the movies. As social network services have been recently popularized, many users obtain and exchange their opinions on social networks. This information is reliable because they have close relationships and trust each other. Most of the users are satisfied with the information. In this paper, we propose a recommendation system based on advanced user modeling using social relationship of users. For the user modeling, both direct and indirect relations are considered and the relation weight between users is calculated by using six degrees of Kevin Bacon. From the experimental results, our proposed social filtering method can achieve better performance than a traditional user-based collaborative filtering method.

Social filtering using social relationship for movie recommendation

Nowadays, ontology has been backbone of Semantic web. However, the current ontologies are based on traditional logic such as first-order logic and description logic. The conceptual formalism of the ontologies cannot be fully representative for imprecise and vague information (e.g. ”rainfall is very heavy”) in many application domains. In this paper, a domain fuzzy ontology is defined clearly, and its components such as fuzzy relation, concrete fuzzy concept, and fuzzy domain concept as well as similarity measures between the components are addressed. Fuzzy ontology integration on concept level using consensus method to solve conflicts among the ontologies is proposed. In particular, the postulates for integration are specified and algorithms for reconciling conflicts among fuzzy concepts in ontology integration are proposed.

Geun-Sik Jo

Papers

Extracting user interests from bookmarks on the web

Social filtering using social relationship for movie recommendation

Fuzzy Ontology Integration Using Consensus to Solve Conflicts on Concept Level

Enhancing performance and accuracy of ontology integration by propagating priorly matchable concepts

Visual tracking based on a unified tracking-and-detection framework with spatial-temporal consistency filtering