- Multidimensional data are getting increasing attention
from researchers for creating better recommender systems
in recent years. Additional metadata provides algorithms
with more details for better understanding the interaction
between users and items. While neighbourhood-based
Collaborative Filtering (CF) approaches and latent factor
models tackle this task in various ways effectively, they
only utilize different partial structures of data. In this
paper, we seek to delve into different types of relations in
data and to understand the interaction between users and
items more holistically. We propose a generic
multidimensional CF fusion approach for top-N item
recommendations. The proposed approach is capable of
incorporating not only localized relations of user-user and
item-item but also latent interaction between all
dimensions of the data. Experimental results show
significant improvements by the proposed approach in
terms of recommendation accuracy.`
(from crpit.com)
(local if available)
