The aim of recommender systems is to provide users with items that could be of their interest. However one of the biggest drawbacks from recommender systems is the so called cold start problem, which occurs when new users or products are added to the system and therefore there is no previous information about them. There are many proposals in the literature that aim to deal with this issue. In some cases the user is required to provide some explicit information about them, which demands some effort on their part. Because of that and due to the great boom of social networks, we will focus on extracting implicit information from user’s social stream. In this paper we will present an approach on which social media data will be used to create a behavioural profile to classify the users and based on this classification will create predictions making use of machine learning techniques such as classification trees and random forests. Thus the user will not have to provide actively any kind of data explicitly but their social media source, alleviating in this way the cold start problem since the system would use this data in order to create user profiles, which will be the input for the engine of the recommender systems. We have carried out numerous experiments, as well as a comparison with some other state-of-the-art new user cold-start algorithms, obtaining very satisfactory results.

推荐系统的目的是为用户提供他们感兴趣的项目。但是，推荐系统的最大缺点之一就是所谓的冷启动问题，当新用户或新产品添加到系统中时，就会发生此问题，因此没有关于它们的先前信息。文献中有许多针对此问题的建议。在某些情况下，要求用户提供一些有关他们的明确信息，这需要他们付出一些努力。因此，由于社交网络的蓬勃发展，我们将专注于从用户的社交流中提取隐式信息。在本文中，我们将提供一种方法，在该方法上，社交媒体数据将用于创建行为配置文件以对用户进行分类，并基于此分类将使用诸如分类树和随机森林之类的机器学习技术来创建预测。因此，用户将不必显式主动提供任何数据，而是提供其社交媒体源，从而缓解了冷启动问题，因为系统将使用此数据来创建用户配置文件，这将成为引擎的输入推荐系统。我们进行了许多实验，并与其他一些最新的新用户冷启动算法进行了比较，获得了非常令人满意的结果。因此，用户将不必显式主动提供任何数据，而是提供其社交媒体源，从而缓解了冷启动问题，因为系统将使用此数据来创建用户配置文件，这将成为引擎的输入推荐系统。我们进行了许多实验，并与其他一些最新的新用户冷启动算法进行了比较，获得了非常令人满意的结果。因此，用户将不必显式主动提供任何数据，而是提供其社交媒体源，从而缓解了冷启动问题，因为系统将使用此数据来创建用户配置文件，这将成为引擎的输入推荐系统。我们进行了许多实验，并与其他一些最新的新用户冷启动算法进行了比较，获得了非常令人满意的结果。