基于集团序方法的推荐系统输出

论文从推荐系统的输出形式出发, 认为系统以top-N形式输出时, N值的大小影响了推荐的质量和推荐个性化. 论文探讨了输出结果差异化的可行性及一般方法, 采用集团序的方法提出以整个子集团作为推荐输出、以子集团内的产品个数作为候选N值的思想, 有效地避免了推荐结果中被推荐产品间差异大, 以及被推荐产品与不被推荐产品之间差异小的问题. 论文构建了推荐系统中一般产品集团序模型, 并针对N值大小进行了产品集团序质量评估, 进而得到了可靠的N值. 这一研究结果不仅丰富了推荐系统的理论成果, 也为推荐输出的个性化研究探索了新的道路.

Output of recommender systems based on aggregative rank

From the output functional of recommender systems, the issue of the top-N was researched in this paper. The value of N affects the recommended quality and personalization. The feasibility and general methods of personalization of the output were studied in this paper. A new method based on aggregative rank was proposed. In the method, the entire sub-group is used as output of recommendation and the number of products in the sub-group is used as candidate value of N, in order to avoid big differences between the recommended products in the recommendation results and small differences between recommended products and un-recommended products. The general aggregative rank model of product was built in this paper. Meanwhile, aggregative rank quality was assessment based on different N, only in order to get a reliable value of N. The research results not only enrich the recommended system theoretical results, but also get a new road for personalization study of recommender systems.