Collaborative recommender systems: Combining effectiveness and efficiency
Collaborative recommender systems: Combining effectiveness and efficiency
复制标题
DOI：
10.1016/j.eswa.2007.05.013
复制
发表时间：
2008-05
期刊：
影响因子：
--
通讯作者：
P. Symeonidis;A. Nanopoulos;A. Papadopoulos;Y. Manolopoulos
中科院分区：
文献类型：
--
作者：
P. Symeonidis;A. Nanopoulos;A. Papadopoulos;Y. Manolopoulos
推荐系统的基础上，他们的操作在过去的用户评级的项目，例如，书籍，CD等的集合。协同过滤（CF）是一个成功的推荐技术，面临的“信息过载”的问题。基于记忆的算法根据最近邻居的偏好进行推荐，基于模型的算法通过首先开发用户评级模型进行推荐。在本文中，我们带来的表面因素，影响CF过程中，以识别现有的错误信念。在准确性方面，通过能够查看“大局”，我们提出了新的方法，大大提高CF算法的性能。例如，我们获得了超过40%的精度相比，广泛使用的CF算法。在效率方面，我们提出了一个基于模型的方法的基础上潜在的语义索引（LSI），减少执行时间至少50%，比经典的CF算法。
Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the “information overload” problem. Memory-based algorithms recommend according to the preferences of nearest neighbors, and model-based algorithms recommend by first developing a model of user ratings. In this paper, we bring to surface factors that affect CF process in order to identify existing false beliefs. In terms of accuracy, by being able to view the “big picture”, we propose new approaches that substantially improve the performance of CF algorithms. For instance, we obtain more than 40% increase in precision in comparison to widely-used CF algorithms. In terms of efficiency, we propose a model-based approach based on latent semantic indexing (LSI), that reduces execution times at least 50% than the classic CF algorithms.