Adaptive recommendation for MOOC with collaborative filtering and time series
Sign Up to like & getrecommendations! Published in 2018 at "Computer Applications in Engineering Education"
DOI: 10.1002/cae.21995
Abstract: Massive Open Online Course (MOOC) has developed rapidly in recent years. However, the low satisfaction and the feelings of loneliness tend to cause more dropouts. A solution called Adaptive Recommendation for MOOC (ARM) is proposed… read more here.
Keywords: collaborative filtering; recommendation; recommendation mooc; adaptive recommendation ... See more keywords
A collaborative filtering algorithm based on correlation coefficient
Sign Up to like & getrecommendations! Published in 2018 at "Neural Computing and Applications"
DOI: 10.1007/s00521-018-3857-7
Abstract: Due to the concise design concept and the superior computing performance, collaborative filtering algorithm has become a hot research field in recommendation systems. Firstly, this paper summarizes the relevant research achievements of collaborative filtering algorithms… read more here.
Keywords: collaborative filtering; based algorithm; filtering algorithm; algorithm based ... See more keywords
Neural embedding collaborative filtering for recommender systems
Sign Up to like & getrecommendations! Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-04920-9
Abstract: The main purpose of collaborative filtering algorithm is to provide a personalized recommender system based on past interactions of each user (e.g., clicks and purchases). Among various collaborative filtering techniques, matrix factorization is widely adopted… read more here.
Keywords: collaborative filtering; recommender; neural embedding; matrix factorization ... See more keywords
Improving collaborative filtering’s rating prediction accuracy by introducing the experiencing period criterion
Sign Up to like & getrecommendations! Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-05460-y
Abstract: Collaborative filtering algorithms take into account users’ tastes and interests, expressed as ratings, in order to formulate personalized recommendations. These algorithms initially identify each user’s “near neighbors,” i.e., users having highly similar tastes and likings.… read more here.
Keywords: collaborative filtering; accuracy; period; rating prediction ... See more keywords
Improved covering-based collaborative filtering for new users’ personalized recommendations
Sign Up to like & getrecommendations! Published in 2020 at "Knowledge and Information Systems"
DOI: 10.1007/s10115-020-01455-2
Abstract: User-based collaborative filtering (UBCF) is widely used in recommender systems (RSs) as one of the most successful approaches, but traditional UBCF cannot provide recommendations with satisfactory accuracy and diversity simultaneously. Covering-based collaborative filtering (CBCF) is… read more here.
Keywords: collaborative filtering; covering based; personalized recommendations; new users ... See more keywords
Distributed representations based collaborative filtering with reviews
Sign Up to like & getrecommendations! Published in 2018 at "Applied Intelligence"
DOI: 10.1007/s10489-018-01406-z
Abstract: Review texts, which have been shown helpful for recommending items for users, are often available in the form of user feedback for items. Despite the success of previous approaches exploring reviews for recommendations, they are… read more here.
Keywords: collaborative filtering; distributed representations; filtering reviews; based collaborative ... See more keywords
Collaborative filtering recommendation algorithm integrating time windows and rating predictions
Sign Up to like & getrecommendations! Published in 2019 at "Applied Intelligence"
DOI: 10.1007/s10489-019-01443-2
Abstract: This paper describes a new collaborative filtering recommendation algorithm based on probability matrix factorization. The proposed algorithm decomposes the rating matrix into two nonnegative matrixes using a predictive rating model. After normalization processing, these two… read more here.
Keywords: time; matrix; filtering recommendation; rating ... See more keywords
A collaborative filtering recommendation algorithm based on the influence sets of e-learning group’s behavior
Sign Up to like & getrecommendations! Published in 2017 at "Cluster Computing"
DOI: 10.1007/s10586-017-1560-6
Abstract: At present, due to use of nearest neighbor query algorithm based on memory, the traditional user-based collaborative filtering (CF) recommendation system has the shortages of poor expandability and lack of stability. On the aspect of… read more here.
Keywords: collaborative filtering; recommendation; algorithm; influence sets ... See more keywords
Adaptable and proficient Hellinger Coefficient Based Collaborative Filtering for recommendation system
Sign Up to like & getrecommendations! Published in 2018 at "Cluster Computing"
DOI: 10.1007/s10586-017-1616-7
Abstract: Currently in many real time applications, recommender systems play a prominent role in helping the customers by selecting the most suitable products of their requirements. Generally the scheme of recommending the products is observed as… read more here.
Keywords: collaborative filtering; hellinger coefficient; recommendation; system ... See more keywords
Two new collaborative filtering approaches to solve the sparsity problem
Sign Up to like & getrecommendations! Published in 2020 at "Cluster Computing"
DOI: 10.1007/s10586-020-03155-6
Abstract: Collaborative filtering which is the most successful technique of the Recommender System, has recently attracted great attention, especially in the field of e-commerce. CF is used to help users find their preferred items by assessing… read more here.
Keywords: collaborative filtering; two new; new collaborative; sparsity problem ... See more keywords
Improving sparsity and new user problems in collaborative filtering by clustering the personality factors
Sign Up to like & getrecommendations! Published in 2018 at "Electronic Commerce Research"
DOI: 10.1007/s10660-018-9287-x
Abstract: In collaborative filtering recommender systems, items recommended to an active user are selected based on the interests of users similar to him/her. Collaborative filtering systems suffer from the ‘sparsity’ and ‘new user’ problems. The former… read more here.
Keywords: collaborative filtering; user; user problems; personality ... See more keywords