Embedded Collaborative Filtering for "Cold Start" Prediction

Yubo Zhou, Ali Nadaf

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.

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