Today's information retrieval applications have become increasingly complex. The Social Book Search (SBS) lab at CLEF 2015 allows evaluating retrieval methods on a complex search task with several textual and non-textual meta-data fields. The challenge is to incorporate the different information types (modalities) into a single ranked list. We build a strong textual baseline and combine it with a document prior based on social signals. Further, we include non-textual modalities in relation to the user preferences using random forest learning to rank. Our experiments show that both the social document prior and the learning to rank approach improve the search results.