Addressing Item-Cold Start Problem in Recommendation Systems using Model Based Approach and Deep Learning
This addresses the issue of making recommendations for new items without usage data, which is a common challenge in recommendation systems, though it appears incremental as it combines existing techniques.
The paper tackles the item-cold start problem in recommendation systems by using a model-based approach with deep learning, specifically predicting latent factors from item descriptions via a convolutional neural network, and reports that it significantly outperforms baseline estimators.
Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past interactions. In this paper, we propose a solution for successfully addressing item-cold start problem which uses model-based approach and recent advances in deep learning. In particular, we use latent factor model for recommendation, and predict the latent factors from item's descriptions using convolutional neural network when they cannot be obtained from usage data. Latent factors obtained by applying matrix factorization to the available usage data are used as ground truth to train the convolutional neural network. To create latent factor representations for the new items, the convolutional neural network uses their textual description. The results from the experiments reveal that the proposed approach significantly outperforms several baseline estimators.