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

/pdf/an-effective-approach-to-generate-wikipedia-infobox-of-movie-5ea1gmf7jw.pdf

An effective approach to generate Wikipedia infobox of movie domain using semi-structured data

Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of contents that are likely to fit their needs One notable challenge in a recommender system is the cold start problem To address this issue, we propose a collaborative approach to user modeling for generating personalized recommendations for users Our approach first discovers useful and meaningful patterns of users, and then enriches a personal model with collaboration from other similar users In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user-based collaborative filtering, and vector space model We present experimental results that show how our model performs better than existing work

A Collaborative Approach to User Modeling for Personalized Content Recommendations

With the spread of the digital library and the web, users can obtain a wide variety of information, and also can access novel content In this environment, finding useful information from a huge amount of available content becomes a time consuming process In this paper, we focus on user modeling for personalization to recommend content relevant to user interests We exploit the data mining techniques for identifying useful and meaningful patterns of users Each user model, collectively called PTP (Personalized Term Pattern), is represented as both interest patterns and disinterest patterns We present empirical experiments using NSF research award datasets to demonstrate our approach and evaluate performance compared with existing methods

Modeling and learning user profiles for personalized content service

The explosive growth of information on the web demands effective intelligent search and filtering methods. Consequently, techniques have been developed that extract conceptual information from the document and use the conceptual information in the user profile to form part of the user's information intent from his/her query. In a similar vein, we build the profile without user interaction, automatically monitoring the user's browsing habits. These profiles, in turn, are used to automatically learn the semantic context of user's information need. These sets of categories can serve as a context to disambiguate the words in the user's query. In this paper, we present a framework for assisting the user in one of the most difficult information retrieval tasks, i.e., that of formulating an effective search query. Our experimental results show that implicit measurements of user interests, combined with the semantic knowledge embedded in a concept hierarchy, can be used effectively to infer the user context and to improve the results of information retrieval.

Exploring the effective search context for the user in an interactive and adaptive way

Most Siamese network-based trackers perform the tracking process without model update, and cannot learn target-specific variation adaptively. Moreover, Siamese-based trackers infer the new state of tracked objects by generating axis-aligned bounding boxes, which contain extra background noise, and are unable to accurately estimate the rotation and scale transformation of moving objects, thus potentially reducing tracking performance. In this paper, we propose a novel Rotation-Scale Invariant Network (RSINet) to address the above problem. Our RSINet tracker consists of a target-distractor discrimination branch and a rotation-scale estimation branch, the rotation and scale knowledge can be explicitly learned by a multi-task learning method in an end-to-end manner. In addtion, the tracking model is adaptively optimized and updated under spatio-temporal energy control, which ensures model stability and reliability, as well as high tracking efficiency. Comprehensive experiments on OTB-100, VOT2018, and LaSOT benchmarks demonstrate that our proposed RSINet tracker yields new state-of-the-art performance compared with recent trackers, while running at real-time speed about 45 FPS.

Geun-Sik Jo

Papers

An effective approach to generate Wikipedia infobox of movie domain using semi-structured data

A Collaborative Approach to User Modeling for Personalized Content Recommendations

Modeling and learning user profiles for personalized content service

Exploring the effective search context for the user in an interactive and adaptive way

RSINet: Rotation-Scale Invariant Network for Online Visual Tracking