The goal of recommender systems (RS) is to provide personalized recommendations of products or services to users facing the problem of information overload on the Web. They provide personalized recommendations that best suit a customer's taste, preferences, and individual needs. With the advent of the Social Web, user generated content has enriched the social dimension of the Web which poses both new possibilities and challenges for RS research. This work deals with the question of how user-provided tagging data can be used to improve the quality of RS. Tag-based recommendations and explanations are the two main areas of contribution in this work. The area of tag-based recommendations deals mainly with the topic of recommending items by exploiting tagging data. A tag recommender algorithm is proposed which can generate highly-accurate tag recommendations in real-time. Furthermore, the concept of user- and item-specific tag preferences is introduced in this work. By attaching feelings to tags users are provided a powerful means to express in detail which features of an item they particularly like or dislike. The area of tag-based explanations, on the other hand, deals with questions of how explanations for recommendations should be communicated to the user in the best possible way. New explanation methods based on personalized and non-personalized tag clouds are introduced.