Abstract: A personalized selection of applications for presentation on a web-based interface can be produced. A first vector can represent one or more first words from a first query. A second query, including the one or more first words and one or more second words, can be transmitted in response to a first determination that a measure of similarity between the first vector and a second vector, which represents the one or more second words, is greater than a threshold. The second vector can be obtained from a knowledge base. A response to the second query can include an identification of a first application. A cluster of applications, including the first application and a second application, can be generated in response to a second determination of an existence of a relationship between the first application and the second application. The personalized selection of applications can be produced based on the cluster.

Abstract: A system and method for optimizing clusters included in an app store are disclosed. The method comprises generating, by one or more proposal servers, one or more cluster proposals, wherein each of the one or more proposal servers executes a proposal algorithm, processing, by a cluster server, the one or more cluster proposals to resolve any conflicts within and across the one or more proposal servers, assigning a priority value to each of the one or more cluster proposals based on a predicted impact of the respective cluster proposal in the app store, forwarding to a review portal a predetermined amount of prioritized cluster proposals for review and approval, and publishing approved prioritized clusters proposals on the app store.

Abstract: Systems and methods are disclosed for targeting effective contributors and identifying high quality contributions. For example, a method may include displaying an advertisement to a potential contributor via an advertising platform, receiving an indication that the potential contributor responded to the advertisement, generating a crowdsourcing exercise that is presented to the contributor, receiving a response (a conversion event) from the contributor to the crowdsourcing exercise, and notifying the advertising platform about the conversion event. As another example, a method may include determining a concept space for a new contribution, obtaining previously correct and incorrect contributions of the contributor in the concept space, and determining an expertise confidence score for the new contribution based on a comparison of the new contribution with the previously correct and incorrect contributions.