Sfile Technology Corporation
5 services found
Sfile Technology Corporation services
Recommender Systems
Recommender systems automate the process of making real-time recommendations to customers. A simple example: an online customer who is browsing a store for one item (e.g. a power drill), places the item in their shopping cart, and is then recommended to buy a complementary item (e.g., a set of drill bits). This example is trivial. Machine learning can go further, often uncovering unexpected buying patterns, based on unforeseen relationships between different customers and between different products.Clustering and Segmentation
Cluster analysis and segmentation represents a purely data driven approach to grouping similar objects, behaviors, or whatever is represented by the data. Traditional marketers segment customers based on easily identified traits, e.g., age, zip code, gender. Machine learning can take into account all available attributes and information, e.g., website visit history, social network activity, mobile access, purchase history as well as ‘traditional’ attributes. The result is far more insightful, actionable and valuable.Predective Analytics
Predictive analytics is the science of analyzing current and historical facts/data to make predictions about future events. Unlike traditional business intelligence practices, which are more backward-looking in nature, predictive analytics is focused on helping companies derive actionable intelligence based on past experience. A typical application is in insurance: predicting which policy holders (or potential policy holders) will make a claim and how long it will be until they make the claim. The more data available on the history of claims and ‘extraneous’ information about the policy holder the more variables a predictive analytics algorithm can take in to account. For example, a machine learning algorithm could easily take into account the impact of when a parent has children on claim rates. Identifying if such a relationship exists (amongst ALL the other possibilities) is too complex for human analysts. It is an automatic process with machine learning.Similarity Search
Similarity search provides a way to find the objects that are the most similar, in an overall sense, to the object(s) of interest. A typical example is that of a doctor finding the top 10 past patients who are most similar to the current patient of interest. This could be used for diagnosis, but also adds the human judgement that some other machine learning methods do not necessarily offer. Another example of approximate similarity search is for finding the song in a database corresponding to a given sound sample, or finding the person in a database corresponding to a face photo. Similarity searches can be thought of as multidimensional analogs to SQL queries. SQL queries are composed of conditions on individual variables, for example “Find all customers whose age is within a certain range and whose income is greater than a certain amount”, whereas similarity searches are more like “Find all the customers most like this one”.