Neighborhood models (NBM s) are the methods widely used for collaborative filtering in
recommender systems. Given a target user and a target item, NBMs find k most similar users …

Data sparsity is one of the biggest problems faced by collaborative filtering used in
recommender systems. Data imputation alleviates the data sparsity problem by inferring …

The cold-start problem is one of the critical challenges in personalized recommender
systems. A lot of existing work has been studied to exploit a user-item rating matrix as well …

A key challenge in recommender systems is how to profile new users. A well-known solution
for this problem is to ask new users to rate a few items to reveal their preferences and to use …

The questions in a crowdsourcing task typically exhibit varying degrees of difficulty and
subjectivity. Their joint effects give rise to the variation in responses to the same question by …

Recommender systems have become a familiar part of our online experiences, suggesting
movies to watch, music to listen to, and books to read, among other things. To make relevant …

Active Learning (AL) has been applied to Recommender Systems so as to elicit ratings from
new users, namely Rating Elicitation for Cold Start Purposes. In most e-commerce systems …

A Recommender System (RS) is a particular type of information filtering system used to
propose relevant items to users. Their successful application in online retail is reflected in …

ScholarWorks@Hanyang University: On Evaluating the Accuracy of Trust-aware
Recommendation Approaches ScholarWorks@Hanyang University 한국어 LIBRARY …

의 행동 이력을 분석하여 선호할 것으로 예상되는상 문제가 발생한다. 풀을 관악하여 천하는
시스템이다. 은인 이 이러한 데이터 희소성 문제를 해결하기 위한 방법 kgkk 대중화됨에 따라 …