基于内容的推荐系统：前沿和趋势

基于内容的推荐系统：前沿和趋势(中文4400字,英文2500字)
Pasquale Lops, Marco de Gemmis and Giovanni Semeraro
摘要：推荐系统以个性化的方式指引用户在众多选择中找到感兴趣的东西。基于内容的推荐系统会发据用户曾经喜欢的产品，从而尝试去推荐类似的产品使其满意。事实上基于内容的推荐系统主要的处理方式在于利用用户已知的偏好、兴趣等属性和物品内容的属性相匹配，以此为用户推荐新的感兴趣的物品。本章概述了各种基于内容的推荐系统，目的是在其各种设计原理和实现方法中理出头绪。本章第一部分介绍了基于内容推荐的基本概念、专业术语、高层次的体系结构和主要的优缺点。第二部分通过详尽描述能够表示物品和用户信息的经典先进技术，给出了几个应用领域内使用的最先进技术的概述。同时，也阐述了一些被广泛使用的学习用户兴趣的技术。最后一部分讨论了推荐系统的趋势和下一代推荐系统的研究方向，其中描述了考虑到在词汇表不断演变情况下用户产生内容(UGC)的作用，以及为用户提供一些偶然性的推荐的挑战，即推荐出平意料地能够让用户感兴趣，而又无法通过其他方法发现的物品。
Content-based Recommender Systems: State of the Art and Trends
Pasquale Lops, Marco de Gemmis and Giovanni Semeraro
Abstract Recommender systems have the effect of guiding users in a personalized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recommender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of contentbased recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.