Currently, most web service recommendation studies concentrate on mining association patterns among services from historical compositions and recommending proper services based on patterns derived. However, latent negative patterns which indicate the inappropriate combinations of services, are mostly ignored. Therefore, by combining additional negative patterns with the already-exploited positive patterns in the large spares network of web services, we present a more comprehensive and accurate model for service recommendation. More specifically, we combine positive and negative composition patterns mined from service annotated tags. The extensive experiments conducted on a real-life dataset show that our method can outperform not only traditional APriori -based recommendation method but also Link Prediction-based one. The experiments on a synthetic dataset show that our method can also be effective to make recommendations in large-scale service network.