Sentence examples for collaborative filtering method from inspiring English sources

In this paper we present a collaborative filtering method which opens up the possibilities of traditional collaborative filtering in two aspects: (1) it enables joint recommendations to groups of users and (2) it enables the recommendations to be restricted to items similar to a set of reference items.

One is based on the user's history, which is a content-based method, and the other is based on group history, which is a collaborative filtering method.

Although the collaborative filtering method can offer diverse recommendations, a simple collaborative based recommender may not meet the specific user's interests.

Similar to the similarity-based collaborative filtering method, our main purpose is also to predict the rating (hat{r}_{m,n}) of the target user (u_{m} in U) for the target item (s_{n} in S) that he/she has not known yet.

Only a handful of studies have investigated automated or semi-automated approaches: Hassan et al. attempted to identify missing medications between patients' medication lists using a collaborative filtering method and Silva et al. proposed a natural language processing- (NLP) based approach to reconcile medications manually identified from clinical notes to structured prescription lists [ 17, 18].

Based on the framework, we propose three solutions by specifying three state-of-the-art collaborative filtering methods, namely Maximum-Margin Matrix Factorization, Regularized Low-rank Matrix Factorization, and Probabilistic Matrix Factorization.

It uses machine learning algorithms, natural language processing and collaborative filtering methods to help determine listener preferences.

Most traditional collaborative filtering methods only considered the rating data in the rating matrix.

Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation.

It uses a combination of machine learning algorithms, natural language processing and collaborative filtering methods to help determine listener preferences, the company says.

The new playlists are built using these various machine learning algorithms, combined with the raw audio analysis from the Music Genome, more traditional collaborative filtering methods, and in-house editorial curation.