Recommender Systems

Context-aware Approach for Restaurant Recommender Systems, the Law and other Social Sciences

This paper addresses the issue of how to effectively use users' historical data in restaurant recommender systems, as opposed to systems, such as FindMe, that only rely on online operations. Towards that end, authors propose a bias-based SVD method as the underlying recommendation algorithm and test it against the traditional item-based collaborative filtering method on the Entrée restaurant dataset. The results are promising as the obtained Root-Mean-Square-Error (RMSE) values reach 0.58 for the SVD and 0.62 for the item-based system. Researchers can extend the transformation from user behaviors to ratings in more application domains other than the restaurant one.[1]

Resources

Notes and References

1. Haoxian Feng, Thomas Tran, “Context-aware Approach for Restaurant Recommender Systems” (Encyclopedia of Information Science and Technology, 4th Edition, Information Resources Management Association, 2018)