Collaborative Filtering (CF) filters the flow of data that can be recommended, by a
Recommender System (RS), to a target user according to his taste and his preferences. The …

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 …

Objectives We review the evidence for tranexamic acid (TXA) for the treatment and
prevention of bleeding caused by surgery, trauma and bleeding disorders. We highlight …

Jaccard index, originally proposed by Jaccard (Bull Soc Vaudoise Sci Nat 37: 241–272,
1901), is a measure for examining the similarity (or dissimilarity) between two sample data …

The recommender systems aim to predict potential demands of users by analyzing their
preferences and provide personalized recommendation services. User preferences can be …

Probabilistic dual hesitant fuzzy set (PDHFS) can better reflect the hesitant attitude and
probability information of decision-makers than existing fuzzy sets. Therefore, it is a more …

Memory-based collaborative filtering is one of the recommendation system methods used to
predict a user's rating or preference by exploring historic ratings, but without incorporating …

Collaborative filtering is one of the most widely used recommendation system approaches.
One issue in collaborative filtering is how to use a similarity algorithm to increase the …

Abstract In Recommendation Systems (RS) and Collaborative Filtering (CF), the similarity
measures have been the operating component upon which CF performance is essentially …

The recommender system is widely used in the field of e-commerce and plays an important
role in guiding customers to make smart decisions. Although many algorithms are available …