KMeans clustering-based recommendation algorithms have been proposed claiming to increase the scalability of recommender systems. One potential drawback of these algorithms is that they perform training offline and hence cannot accommodate the incremental updates with the arrival of new data, making them unsuitable for the dynamic environments. From this line of research, a new clustering algorithm called One-Pass is proposed, which is a simple, fast, and accurate. We show empirically that the proposed algorithm outperforms K-Means in terms of recommendation and training time while maintaining a good level of accuracy.
Making e ective recommendations from a domain consisting of millions of ratings is a major research challenge in the application of machine learning. Kernel Mapping Recommender (KMR) algorithms have been proposed providing state-of-the-art performance. In this paper, we show how context information can be added to KMR algorithms. We consider the trusted friends of a user as their social context and show how this information can be used to provide more personalised, refined, and trustworthy recommendations. The limited set of friends; however, restricts the amount of data available to create useful recommendations. This paper sheds light on this issue and specifically on the amount of friends necessary to get satisfactory recommendations.
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