Abstract
Recommender systems are becoming increasingly popular. As these systems become commonplace and the number of users increases, it will become important for these systems to be able to cope with a large and diverse set of users whose recommendation needs may be very different from each other. In particular, large scale recommender systems will need to ensure that users’ requests for recommendations can be answered with low response times and high throughput. In this paper, we explore how to use caches and cached data mining to improve the performance of recommender systems by improving throughput and reducing response time for providing recommendations. We describe the structure of our cache, which can be viewed as a prefetch cache that prefetches all types of supported recommendations, and how it is used in our recommender system.We also describe the results of our empirical study to measure the efficacy of our cache.
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Sheth, S., Kaiser, G. (2013). Towards Using Cached Data Mining for Large Scale Recommender Systems. In: Gaol, F. (eds) Recent Progress in Data Engineering and Internet Technology. Lecture Notes in Electrical Engineering, vol 156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28807-4_49
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DOI: https://doi.org/10.1007/978-3-642-28807-4_49
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