Minimal Interaction Search in Recommender Systems
Minimal Interaction Search in Recommender Systems
复制标题
推荐系统中的最小交互搜索
DOI：
10.1145/2678025.2701367
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
S. Berkovsky
中科院分区：
文献类型：
--
作者：
B. Kveton;S. Berkovsky
While numerous works study algorithms for predicting item ratings in recommender systems, the area of the user-recommender interaction remains largely under-explored. In this work, we look into user interaction with the recommendation list, aiming to devise a method that allows users to discover items of interest in a minimal number of interactions. We propose generalized linear search (GLS), a combination of linear and generalized searches that brings together the benefits of both approaches. We prove that GLS performs at least as well as generalized search and compare our method to several baselines and heuristics. Our evaluation shows that GLS is liked by the users and achieves the shortest interactions.
