Service composition remains an important topic where recommendation is widely recognized as a core mechanism. Existing works on service recommendation typically examine either association rules from mashup-service usage records, or latent topics from service descriptions. This paper moves one step further, by studying latent topic models over service collaboration history. A concept of service co-occurrence topic is coined, equipped with a mechanism developed to construct service co-occurrence documents. The key idea is to treat each service as a document and its co-occurring services as the bag of words in that document. Four gauges are constructed to measure self-co-occurrence of a specific service. A theoretical approach, Service Co-occurrence LDA (SeCo-LDA), is developed to extract latent service co-occurrence topics, including representative services and words, temporal strength, and services' impact on topics. Such derived knowledge of topics will help to reveal the trend of service composition, understand collaboration behaviors among services and lead to better service recommendation. To verify the effectiveness and efficiency of our approach, experiments on a real-world data set were conducted. Compared with methods of Apriori, content matching based on service description, and LDA using mashup-service usage records, our experiments show that SeCo-LDA can recommend service composition more effectively, i.e., 5% better in terms of Mean Average Precision than baselines.

Service ecosystem consists of all kinds of services, and some of them may be composed by developers to create new mashups. Existing work on service recommendation and composition mine either frequent patterns from mashup-service usage records, or latent topics from service metadata. In this paper, we propose Service Co-occurrence LDA (SeCo-LDA), a novel approach that mines latent topic models over service co-occurrence patterns. The key idea is to treat each service as a document, and its bag of co-occurring services as the bag of words in that document. Using this model, we can analyze such service co-occurrence documents with a probabilistic topic model. We show how to derive service co-occurrence topics, and then validate our model on the real-world ProgrammableWeb.com dataset. We illustrate that SeCo-LDA can discover meaningful latent service composition patterns including their temporal strength and services' impacts, which conventional Apriori can not reveal. Comparing with Apriori, content matching based on service description and LDA directly using mashup-service usage records, we have demonstrated that SeCo-LDA can recommend service composition more effectively, 5% better in terms of MAP than the baseline approach.

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.