# Addressing Item-Cold Start Problem in Recommendation Systems using Model Based Approach and Deep Learning

## TL;DR

This paper presents a deep learning-based method to address the item-cold start problem in recommendation systems by predicting latent factors from textual descriptions, significantly improving recommendation quality for new items.

## Contribution

The paper introduces a novel approach combining matrix factorization and convolutional neural networks to effectively handle item-cold start in recommendation systems.

## Key findings

- Outperforms baseline estimators in experiments
- Uses CNN to predict latent factors from text descriptions
- Effectively addresses item-cold start problem

## Abstract

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.