The product is an engine that automatically gathers site's content to analyze the textual and visitor behavior information of each item, which is then used to impact the recommendations seen by users.
Product content engine then looks at the contextual, behavioral, and social data of the user, the selected content, and the content that the user has designated as valuable, and sends back the strongest recommendations from the intersection of those inputs.
The project is created in Python as a programming language and MySQL as a database server.
Loomia is a Internet technology company based in San Francisco, California in the United States. Loomia offers a module that recommends content on a Web site.
The company is part of a growing Internet trend that aims to bridge the gap between technological capabilities and user intents. Loomia's technology analyzes the content on the web site, as well as user behaviors and social contexts to offer additional content that reflects user's interests.
The technology is reported to find the overlap between these diverse datasets as well as matching it against publisher content preferences. In doing so, Loomia's technology attempts to surface publishers' most valuable content and recommend articles and videos that matter to users.
Loomia has worked with major media companies to optimize their web site content such as Time.com, The Wall Street Journal, Forbes, CNET, US News & World Report, and others. The content recommendation module can be found on these sites under the header of "People Who Read This Also Read."
The main objective of the project was to determine the project information and to test the software components of the application. The list of recommended requirements for test (high level), recommendation and description of the testing strategies were employed. Our team also provided the estimation of test efforts and the list of the test project deliverable elements, and identified the required resources.
Test cycle system: Buildbot.