Inspired by the use of PageRank algorithms in document ranking, we develop and evaluate a cluster-based PageRank algorithm to re-rank information retrieval (IR) output with the objective of improving ad hoc search effectiveness. Unlike existing work, our methods exploit recommender techniques to extract the correlation between documents and apply detected correlations in a cluster-based PageRank algorithm to compute the importance of each document in a dataset. In this study two popular recommender techniques are examined in four proposed PageRank models to investigate the effectiveness of our approach. Comparison of our methods with strong baselines demonstrates the solid performance of our approach. Experimental results are reported on an extended version of the FIRE 2011 personal information retrieval (PIR) data collection which includes topically related queries with click-through data and relevance assessment data collected from the query creators. The search logs of the query creators are categorized based on their different topical interests. The experimental results show the significant improvement of our approach compared to results using standard IR and cluster-based PageRank methods.