Social recommender systems exploit two sources of information for making recommendations, the historical rating behavior of users, and the social connections among them. The basic assumption is that if two users are friends, they are likely to share similar preferences. Many recommendation approaches are based on such correlations between the rating and the social behavior of users. However, there is little work in studying whether there actually exist such correlations and how strong they are. In our work, we look at the two views of user behavior, their social connections, and their history of ratings, and investigate two research questions. The first examines if strong activity in one view, e.g., having many friends, implies strong activity in the other view, e.g., having rated many items. The second investigates whether high similarity in one view, e.g., network similarity, implies high similarity in the other view, e.g., rating similarity. We employ various notions of activity and similarity, and identify those that appear to have the stronger impact. Specifically, to some degree, we find that rating behavior determines social behavior, and that the opposite relationship is weaker.

中文翻译：

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活动和相似性在社会推荐系统中的评级和社会行为中的作用

社交推荐系统利用两种信息来源进行推荐，即用户的历史评分行为以及他们之间的社交联系。基本假设是，如果两个用户是朋友，他们很可能有相似的偏好。许多推荐方法都是基于评级和用户社交行为之间的这种相关性。然而，在研究是否真的存在这种相关性以及它们有多强方面的工作很少。在我们的工作中，我们着眼于用户行为、他们的社交关系和他们的评分历史的两种观点，并调查了两个研究问题。第一个检查在一个视图中的强活动（例如，有很多朋友）是否意味着在另一个视图中的强活动（例如，对许多项目进行评分）。第二个调查研究一个视图中的高相似性（例如网络相似性）是否意味着另一视图中的高相似性（例如评级相似性）。我们采用各种活动和相似性概念，并确定那些似乎具有更强影响的概念。具体而言，在某种程度上，我们发现评级行为决定社会行为，而相反的关系较弱。