Facing the cold start problem in recommender systems
Facing the cold start problem in recommender systems
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DOI：
10.1016/j.eswa.2013.09.005
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发表时间：
2014-03-01
影响因子：
8.5
通讯作者：
Hadjiefthymiades, Stathes
中科院分区：
文献类型：
--
作者：
Lika, Blerina;Kolomvatsos, Kostas;Hadjiefthymiades, Stathes
推荐系统（RS）旨在向用户提供针对特定项目的个性化推荐（例如，音乐、书籍）。流行的技术包括基于内容的（CB）模型和协同过滤（CF）方法。在本文中，我们讨论了RS中一个非常重要的问题：冷启动问题。这个问题与新用户或新项目的推荐有关。对于新用户，系统没有关于其偏好的信息，以便进行推荐。我们提出了一个模型，其中众所周知的分类算法结合相似性技术和预测机制提供了必要的手段检索建议。所提出的方法将分类方法在一个纯粹的CF系统，而人口统计数据的使用有助于识别具有类似行为的其他用户。我们的实验表明，所提出的系统的性能，通过大量的实验。我们采用了GroupLens研究小组提供的众所周知的数据集。我们揭示了所提出的解决方案的优点，在不同的实验方案提供了令人满意的数值结果。(C)2013爱思唯尔有限公司版权所有。
A recommender system (RS) aims to provide personalized recommendations to users for specific items (e.g., music, books). Popular techniques involve content-based (CB) models and collaborative filtering (CF) approaches. In this paper, we deal with a very important problem in RSs: The cold start problem. This problem is related to recommendations for novel users or new items. In case of new users, the system does not have information about their preferences in order to make recommendations. We propose a model where widely known classification algorithms in combination with similarity techniques and prediction mechanisms provide the necessary means for retrieving recommendations. The proposed approach incorporates classification methods in a pure CF system while the use of demographic data help for the identification of other users with similar behavior. Our experiments show the performance of the proposed system through a large number of experiments. We adopt the widely known dataset provided by the GroupLens research group. We reveal the advantages of the proposed solution by providing satisfactory numerical results in different experimental scenarios. (C) 2013 Elsevier Ltd. All rights reserved.