A similarity metric designed to speed up, using hardware, the recommender systems k-nearest neighbors algorithm

作者：Bobadilla Jesus^*; Ortega Fernando; Hernando Antonio; Glez de Rivera Guillermo

来源：Knowledge-Based Systems, 2013, 51: 27-34.

DOI：10.1016/j.knosys.2013.06.010

• Similarity measure
• Metric
• Collaborative filtering
• Memory based
• Recommender system

A significant number of recommender systems utilize the k-nearest neighbor (kNN) algorithm as the collaborative filtering core. This algorithm is simple; it utilizes updated data and facilitates the explanations of recommendations. Its greatest inconveniences are the amount of execution time that is required and the non-scalable nature of the algorithm. The algorithm is based on the repetitive execution of the selected similarity metric. In this paper, an innovative similarity metric is presented: HwSimilarity. This metric attains high-quality recommendations that are similar to those provided by the best existing metrics and can be processed by employing low-cost hardware circuits. This paper examines the key design concepts and recommendation-quality results of the metric. The hardware design, cost of implementation, and improvements achieved during execution are also explored.

• 出版日期2013-10