Recently social collaboration projects such as Wikipedia and Flickr have been gaining popularity, and more and more social tag information is being accumulated. In this study, we demonstrate how to effectively use social tags created by humans to find similar items. We create a query-by-example interface for finding similar items through offering examples as a query. Our work aims to measure the similarity between a query, expressed as a group of items, and another item through utilizing the tag information. We show that using human-generated tags to find similar items has at least two major challenges: popularity bias and the missing tag effect. We propose several approaches to overcome the challenges. We build a prototype website allowing users to search over all entries in Wikipedia based on tag information, and then collect 600 valid questionnaires from 69 students to create a benchmark for evaluating our algorithms based on user satisfaction. Our results show that the presented techniques are promising and surpass the leading commercial product, Google Sets, in terms of user satisfaction.