Empirical Analysis of Attribute-Aware Recommender System Algorithms Using Synthetic Data

As the amount of online shoppers grows rapidly, the need of recommender systems for e-commerce sites are demanding, especially when the number of users and products being offered online continues to increase dramatically. There have been many ongoing researches on recommender systems and in investigating recommendation algorithms that could optimize the recommendation quality. However, adequate and public datasets of users and products have always been demanding to better evaluate recommender system algorithms. Yet, the amount of public data, especially data containing adequate content information (attributes) is limited. When evaluating recommendation algorithms, it is important to observe the behavior of the algorithm as the characteristic of data varies. Synthetic data would allow the application of systematic changes on the data which cannot be done with real-life data. Although studies on synthetic data for the use of recommender systems have been investigated, artificial data wit...

• Type: Journal
• Year: 2006
• Where: JCP
• Authors: Karen H. L. Tso, Lars Schmidt-Thieme
• Keywords: Algorithms, JCP 2006, Recommender Systems, Synthetic Data