Embedded Collaborative Filtering for "Cold Start" Prediction
This addresses the cold start problem for recommender systems, offering a solution without needing extra costly information, though it appears incremental as it adapts an existing method to a new context.
The paper tackled the 'Cold Start' problem in recommender systems by proposing Embedded Collaborative Filtering (ECF), which combines Word2Vec with collaborative filtering using only implicit data, and showed that ECF outperforms other state-of-the-art approaches in cold start scenarios.
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.