@conference{243,
  keywords = {Cold-Start, Deep learning, Recommender Systems},
  author = {Muhammad Ikram and Anban Pillay and Edgar Jembere},
  title = {Using social networks to enhance a deep learning approach to solve the cold-start problem in recommender systems},
  abstract = {The Cold-Start problem refers to the initial sparsity of data available to Recommender Systems that leads to poor recommendations to users. This research compares a Deep Learning Approach, a Deep Learning Approach that makes use of social information and Matrix Factorization. The social information was used to form communities of users. The intuition behind this approach is that users within a given community are likely to have similar interests. A community detection algorithm was used to group users. Thereafter a deep learning model was trained on each community. The comparative models were evaluated on the Yelp Round 9 Academic Dataset. The dataset was pruned to consist only of users with at least 1 social link. The evaluation metrics used were Mean Squared Error (MSE) and Mean Absolute Error (MAE). The evaluation was carried out using 5-fold cross-validation. The results showed that the use of social information improved on the results achieved from the Deep Learning Approach, and grouping users into communities was advantageous. However, the Deep Learning Approach that made use of social information did not outperform SVD++, a state of the art approach for recommender systems. However, the new approach shows promise for improving Deep Learning models.},
  year = {2019},
  journal = {Forum for Artificial Intelligence Research (FAIR2019)},
  chapter = {173-184},
  month = {4/12-6/12},
  publisher = {CEUR},
  address = {Cape Town},
  url = {http://ceur-ws.org/Vol-2540/FAIR2019_paper_51.pdf},
}