Initializing Matrix Factorization Methods on Implicit Feedback Databases

Download Now
Provided by: Journal of Universal Computer Science
Topic: Big Data
Format: PDF
The implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is a much harder task than the explicit feedback based recommendation problem, due to the inherent uncertainty of the interpretation of such user feedbacks. Recently, implicit feedback problem is being received more attention, as application oriented research gets more attractive within the field. This paper focuses on a common matrix factorization method for the implicit problem and investigates if recommendation performance can be improved by appropriate initialization of the feature vectors before training.
Download Now

Find By Topic