We propose a new robust relevance model that can be applied to both pseudo feedback and true relevance feedback in the language-modeling framework for document retrieval. There are three main differences between our new relevance model and the Lavrenko-Croft relevance model. The proposed model brings back the original query into the relevance model by treating it as a short, special document, in addition to a number of top ranked documents returned from the first round retrieval for pseudo feedback, or a number of relevant documents for true relevance feedback. Second, instead of using a uniform prior as in the original relevance model, documents are assigned with different priors according to their lengths (in terms) and ranks in the first round retrieval. Third, the probability of a term in the relevance model is further adjusted by its probability in a background language model. In both cases, we have compared the performance of our model to that of the two baselines: the original relevance model and a linear combination model. Our experimental results show that the proposed new model outperforms both of the two baselines in terms of mean average precision.