A collaborative filtering approach to mitigate the new user cold start problem
A collaborative filtering approach to mitigate the new user cold start problem
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DOI：
10.1016/j.knosys.2011.07.021
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发表时间：
2012-02-01
影响因子：
8.8
通讯作者：
Bernal, Jesus
中科院分区：
文献类型：
--
作者：
Bobadilla, Jesus;Ortega, Fernando;Bernal, Jesus
新用户冷启动问题代表了推荐系统中的一个严重问题，因为它可能导致新用户流失，这些新用户由于在第一阶段收到的推荐缺乏准确性而决定停止使用该系统，而他们尚未投出大量选票来反馈给推荐系统的协同过滤核心。因此，设计新的相似性度量尤为重要，该度量可以为投票很少的用户提供更精确的结果。本文提出了一种使用基于神经学习的优化完善的新相似性度量，其超过了当前度量所获得的最佳结果。该指标已在 Netflix 和 Movielens 数据库上进行了测试，在准确性方面取得了重大改进。应用于新用户冷启动情况时的精确度和召回率。该论文包括数学形式化，描述了如何使用留一法交叉验证来获取推荐系统的主要质量度量。 (C) 2011 Elsevier B.V. 保留所有权利。
The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system due to the lack of accuracy in the recommendations received in that first stage in which they have not yet cast a significant number of votes with which to feed the recommender system's collaborative filtering core. For this reason it is particularly important to design new similarity metrics which provide greater precision in the results offered to users who have cast few votes. This paper presents a new similarity measure perfected using optimization based on neural learning, which exceeds the best results obtained with current metrics. The metric has been tested on the Netflix and Movielens databases, obtaining important improvements in the measures of accuracy. precision and recall when applied to new user cold start situations. The paper includes the mathematical formalization describing how to obtain the main quality measures of a recommender system using leave-one-out cross validation. (C) 2011 Elsevier B.V. All rights reserved.