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Using profile expansion techniques to alleviate the new user problem

机译：使用配置文件扩展技术来缓解新用户问题

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摘要

Collaborative Filtering techniques have become very popular in the last years as an effective method to provide personalized recommendations. They generally obtain much better accuracy than other techniques such as content-based filtering, because they are based on the opinions of users with tastes or interests similar to the user they are recommending to. However, this is precisely the reason of one of its main limitations: the cold-start problem. That is, how to recommend new items, not yet rated, or how to offer good recommendations to users they have not information about. For example, because they have recently joined the system. In fact, the new user problem is particularly serious, because an unsatisfied user may stop using the system before it could even collect enough information to generate good recommendations. In this article we tackle this problem with a novel approach called "profile expansion", based on the query expansion techniques used in Information Retrieval. In particular, we propose and evaluate three kinds of techniques: item-global, item-local and user-local. The experiments we have performed show that both item-global and user-local offer outstanding improvements in precision, up to 100%. Moreover, the improvements are statistically significant and consistent among different movie recommendation datasets and several training conditions.
机译：近年来，作为提供个性化建议的有效方法，协作过滤技术已变得非常流行。与其他技术（例如基于内容的过滤）相比，它们通常获得更好的准确性，因为它们基于具有与他们推荐的用户相似的口味或兴趣的用户的意见。但是，这恰恰是其主要局限之一的原因：冷启动问题。也就是说，如何推荐尚未评级的新项目，或者如何向尚无相关信息的用户提供良好的建议。例如，因为他们最近加入了系统。实际上，新用户的问题特别严重，因为不满意的用户可能在使用该系统之前甚至可以收集足够的信息以生成良好的建议，然后停止使用该系统。在本文中，我们基于信息检索中使用的查询扩展技术，使用一种称为“配置文件扩展”的新颖方法来解决此问题。特别是，我们提出并评估了三种技术：项目全局，项目本地和用户本地。我们执行的实验表明，无论是全局商品还是本地用户商品，其精度都可显着提高，最高可达100％。此外，改进在统计上是有意义的，并且在不同的电影推荐数据集和几种训练条件之间保持一致。

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  • 来源
    《Information Processing & Management》 |2013年第3期|659-672|共14页
  • 作者

    Vreixo Formoso; Diego Fernandez; Fidel Cacheda; Victor Carneiro;

  • 作者单位

    Department of Information and Communication Technologies, University of A Coruna, Facultad de Informatica, Campus de Elvina s, 15071 A Coruna, Spain;

    Department of Information and Communication Technologies, University of A Coruna, Facultad de Informatica, Campus de Elvina s, 15071 A Coruna, Spain;

    Department of Information and Communication Technologies, University of A Coruna, Facultad de Informatica, Campus de Elvina s, 15071 A Coruna, Spain;

    Department of Information and Communication Technologies, University of A Coruna, Facultad de Informatica, Campus de Elvina s, 15071 A Coruna, Spain;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Collaborative Filtering; Profile expansion; Cold-start;

    机译：协同过滤配置文件扩展;冷启动;

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