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Using Concept Lattice for Personalized Recommendation System Design

机译：使用概念格进行个性化推荐系统设计

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摘要

A novel personalized recommendation system (PRS) based on concept lattice is proposed and used to discover valuable information according to users' requirements and interests quickly and efficiently. The system is divided into the offline part and the online part. In the offline part, the formal context and the concept lattice are constructed from the transaction database, and the association rules based on concept lattice are extracted and stored in the rule library. The new added data are used to update the concept lattice and the rule library regularly. In the online part, the behavior data of target user, the concept lattice and the rule library are used to calculate the ordered recommendation results, which are returned to the user. There are two recommendation methods in the online part, which are recommendations based on association rules and collaborative filtering recommendation. Because of the natural advantages of the concept lattice in data processing and analysis, the PRS we designed possesses better precision and faster response capability, as compared with conventional recommendation system.
机译：提出了一种新颖的基于概念格的个性化推荐系统（PRS），用于根据用户的需求和兴趣快速发现有价值的信息。系统分为离线部分和在线部分。在离线部分，从事务数据库构造形式上下文和概念格，并提取基于概念格的关联规则并将其存储在规则库中。新添加的数据用于定期更新概念格和规则库。在在线部分中，目标用户的行为数据，概念格和规则库用于计算排序的推荐结果，并返回给用户。在线部分中有两种推荐方法，即基于关联规则的推荐和协作过滤推荐。由于概念格在数据处理和分析中的天然优势，与常规推荐系统相比，我们设计的PRS具有更好的精度和更快的响应能力。

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