Survey of similarity functions on neighborhood-based collaborative filtering
H Khojamli, J Razmara - Expert Systems with Applications, 2021 - Elsevier
Today, recommender systems play a vital role in the acceleration of searches by internet
users to find what they are interested in. Among the strategies proposed for recommender …
users to find what they are interested in. Among the strategies proposed for recommender …
Recommender systems: an overview, research trends, and future directions
Recommender system (RS) has emerged as a major research interest that aims to help
users to find items online by providing suggestions that closely match their interest. This …
users to find items online by providing suggestions that closely match their interest. This …
Boosting the item-based collaborative filtering model with novel similarity measures
Collaborative filtering (CF), one of the most widely employed methodologies for
recommender systems, has drawn undeniable attention due to its effectiveness and …
recommender systems, has drawn undeniable attention due to its effectiveness and …
Optimized recommendations by user profiling using apriori algorithm
Collaborative filtering has been the most straightforward and most preferable approach in
the recommender systems. This technique recommends an item to a target user from the …
the recommender systems. This technique recommends an item to a target user from the …
An improved item-based collaborative filtering using a modified Bhattacharyya coefficient and user–user similarity as weight
Item-based filtering technique is a collaborative filtering algorithm for recommendations.
Correlation-based similarity measures such as cosine similarity, Pearson correlation, and its …
Correlation-based similarity measures such as cosine similarity, Pearson correlation, and its …
Aggregated Relative Similarity (ARS): a novel similarity measure for improved personalised learning recommendation using hybrid filtering approach
To improve the effectiveness of online learning, the learning materials recommendation is
required to be personalised to the learner material recommendations must be personalized …
required to be personalised to the learner material recommendations must be personalized …
Collaborative filtering in recommender systems: Technicalities, challenges, applications, and research trends
The rapid development and extensive use of recommender systems (RSs) have changed
the face of online service experience. The enormous data generated and the complexity …
the face of online service experience. The enormous data generated and the complexity …
An overlap** clustering approach for precision, diversity and novelty-aware recommendations
Recommender systems aim to provide users with recommendations of quality. New
evaluation metrics such as diversity, have taken an increasing interest in a wide spectrum of …
evaluation metrics such as diversity, have taken an increasing interest in a wide spectrum of …
An improved similarity calculation method for collaborative filtering-based recommendation, considering neighbor's liking and disliking of categorical attributes of items
Similarity measures play an important role in the accuracy of collaborative filtering based
recommendation. Due to non-availability of adequate co-rated users, the accuracy of …
recommendation. Due to non-availability of adequate co-rated users, the accuracy of …
Utilizing alike neighbor influenced similarity metric for efficient prediction in collaborative filter-approach-based recommendation system
The most popular method collaborative filter approach is primarily used to handle the
information overloading problem in E-Commerce. Traditionally, collaborative filtering uses …
information overloading problem in E-Commerce. Traditionally, collaborative filtering uses …