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A Method for Collaborative Recommendation in Document Retrieval Systems

机译:文献检索系统中的协同推荐方法

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The most common problem in the context of recommendation systems is "cold start" problem which occurs when new product is recommended or a new user becomes to the system. A great part of systems do not personalize a user until they gather sufficient information. In this paper a novel method for recommending a profile for a new user based only on knowledge about a few demographic data is proposed. The method merges a content-based approach with collaborative recommendation. The main objective was to show that based on knowledge about other similar users, the system can classify a new user based on subset of demographic data and recommend him a non-empty profile. Using the proposed profile, the user will obtain personalized documents. A methodology of experimental evaluation was presented and simulations were performed. The preliminary experiments have shown that the most important demographic attributes are gender, age, favorite browser and level of education.
机译:推荐系统中最常见的问题是“冷启动”问题,当推荐新产品或新用户加入系统时发生。在收集足够的信息之前,大部分系统不会对用户进行个性化设置。在本文中,提出了一种仅基于有关一些人口统计学数据的知识为新用户推荐配置文件的新颖方法。该方法将基于内容的方法与协作推荐相结合。主要目的是表明,基于对其他类似用户的了解,系统可以根据人口统计数据的子集对新用户进行分类,并向其推荐非空配置文件。使用建议的配置文件,用户将获得个性化文档。提出了一种实验评估的方法,并进行了仿真。初步实验表明,最重要的人口统计属性是性别,年龄,喜欢的浏览器和受教育程度。

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