Recommender systems are efficient tools for filtering online information, which is
widespread owing to the changing habits of computer users, personalization trends, and …

Recommender systems have become an important research field since the emergence of
the first paper on collaborative filtering in the mid-1990s. Although academic research on …

Many online shop** malls in which explicit rating information is not available still have
difficulty in providing recommendation services using collaborative filtering (CF) techniques …

Social media technologies have greatly facilitated the creation of many types of user-
generated information, eg, product rating information can be used to generate preference …

As one of the collaborative filtering (CF) techniques, memory-based CF technique which
recommends items to users based on rating information of like-minded users (called …

摘要互联网技术的迅猛发展把我们带进了信息爆炸的时代. 海量信息的同时呈现,
一方面使用户很难从中发现自己感兴趣的部分, 另一方面也使得大量少人问津的信息成为网络中 …

Collaborative filtering is a popular recommendation technique, which suggests items to
users by exploiting past user-item interactions involving affinities between pairs of users or …

Matrix-Factorization (MF) is an accurate and scalable approach for collaborative filtering
(CF)-based recommender systems. The performance of matrix MF methods depends on how …

In the most studies of the past, only purchase data of users were used in e-commerce
recommender system, while navigational and behavioral pattern data were not utilized …

Most collaborative filtering recommendation algorithms use crisp ratings to represent the
users' preferences. However, users' preferences are subjective and changeable, crisp …