  Using only implicit data, many recommender systems fail in general to provide
a precise set of recommendations to users with limited interaction history.
This issue is regarded as the "Cold Start" problem and is typically resolved by
switching to content-based approaches where extra costly information is
required. In this paper, we use a dimensionality reduction algorithm, Word2Vec
(W2V), originally applied in Natural Language Processing problems under the
framework of Collaborative Filtering (CF) to tackle the "Cold Start" problem
using only implicit data. This combined method is named Embedded Collaborative
Filtering (ECF). An experiment is conducted to determine the performance of ECF
on two different implicit data sets. We show that the ECF approach outperforms
other popular and state-of-the-art approaches in "Cold Start" scenarios.
