  Recently, Deep Neural Networks (DNNs) have been widely introduced into
Collaborative Filtering (CF) to produce more accurate recommendation results
due to their capability of capturing the complex nonlinear relationships
between items and users.However, the DNNs-based models usually suffer from high
computational complexity, i.e., consuming very long training time and storing
huge amount of trainable parameters. To address these problems, we propose a
new broad recommender system called Broad Collaborative Filtering (BroadCF),
which is an efficient nonlinear collaborative filtering approach. Instead of
DNNs, Broad Learning System (BLS) is used as a mapping function to learn the
complex nonlinear relationships between users and items, which can avoid the
above issues while achieving very satisfactory recommendation performance.
However, it is not feasible to directly feed the original rating data into BLS.
To this end, we propose a user-item rating collaborative vector preprocessing
procedure to generate low-dimensional user-item input data, which is able to
harness quality judgments of the most similar users/items. Extensive
experiments conducted on seven benchmark datasets have confirmed the
effectiveness of the proposed BroadCF algorithm
