To mitigate the problem of over-dependence of a pseudo-relevance feedback algorithm on the top-M document set, we make use of a set of equivalence classes of queries rather than one single query. These query equivalents are automatically constructed either from a) a knowledge base of prior distributions of terms with respect to the given query terms, or b) iteratively generated from a relevance model of term distributions in the absence of such priors. These query variants are then used to estimate the retrievability of each document with the hypothesis that documents that are more likely to be retrieved at top-ranks for a larger number of these query variants are more likely to be effective for relevance feedback. Results of our experiments show that our proposed method is able to achieve substantially better precision at top-ranks (e.g. higher nDCG@5 and P@5 values) for ad-hoc IR and points-of-interest (POI) recommendation tasks.