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首页> 外文期刊>Data & Knowledge Engineering >Multi-view fuzzy information fusion in collaborative filtering recommender systems: Application to the urban resilience domain
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Multi-view fuzzy information fusion in collaborative filtering recommender systems: Application to the urban resilience domain

机译：协同过滤推荐系统中的多视图模糊信息融合：在城市弹性域中的应用

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摘要

Recommender systems play an increasingly important role in on-line web services for the personalization and recommendation of content to individual users. The quantity and quality of user-based information has progressed presenting the opportunity to further tailor recommendations to users based on feature view integration. In this work, we propose a hybrid framework which combines a collaborative filtering recommendation system with fuzzy decision-making approaches (based on the use of aggregation functions) to improve the accuracy of domain specific recommendations. We extend upon the classical, neighborhood-based collaborative filtering process by conflating preference information with user-profile data in the recommendation process. This is performed using intelligent information fusion techniques whereby Ordered Weighted Averaging (OWA) operators and uninorm aggregation functions are implemented in the fusion of multiple views of pairwise similarity degrees between users. To address the shortcoming of generating sensible recommendations to cold users, we incorporate a novel weighting scheme based on fuzzy set modeling within the uninorm-based aggregation of similarity views. We finally outline the application of the proposed approach through an empirical study based in the Urban Resilience domain, along with an example to movie recommendation.
机译：推荐系统在在线Web服务中扮演着越来越重要的角色，用于个性化和向单个用户推荐内容。基于用户的信息的数量和质量已经取得了进步，这为基于功能视图集成为用户进一步定制建议提供了机会。在这项工作中，我们提出了一个混合框架，该框架将协作过滤推荐系统与模糊决策方法（基于聚集函数的使用）相结合，以提高特定领域推荐的准确性。我们通过在推荐过程中将偏好信息与用户个人资料数据相结合，扩展了基于邻域的经典协作过滤过程。这是使用智能信息融合技术执行的，从而在用户之间成对相似度的多个视图的融合中实现了有序加权平均（OWA）运算符和单一聚合功能。为了解决向冷用户生成明智建议的缺点，我们在基于相似度的基于非标准的聚合中结合了基于模糊集建模的新颖加权方案。最后，我们通过基于城市弹性领域的经验研究概述了该方法的应用，并给出了电影推荐示例。

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