The problem of user's interest to item matching is at the core of recommendation systems and search engines. This problem is well studied in different contexts such as item, document, music and movie recommendations. For the purpose of recommendation these systems store the context or the meta-data information about the item of interest (e.g. user rating for books, tags, price etc). However, the general approaches for finding relevant items for recommendation cannot be directly applied in the case when the context or meta-data information about the item of interest is missing. In this paper we describe an algorithmic approach to handle this problem of missing context for items. In the proposed approach we have extended the context of user's interest and developed an unsupervised algorithm to find the items of interest for the user. Finally the items are ranked based on their relevance to the user's interest. We study this problem in the domain of dataset recommendation where the meta-data information about the datasets is missing due to lack of coherent and complete repository for the research datasets. We evaluate the performance of the proposed framework with real world dataset consisting of 20 user queries. We find that the proposed framework can recommend datasets for user queries with a recall of 90% in the top-4 recommendations. We also compared the performance of the dataset finding algorithm with the state of art supervised classification approach. We get a significant improvement of 36% using the proposed algorithm.