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User Item recommendations are a simple way to increase sales by making product suggestions to customers based on historical behavior. This can be applied not only to e-commerce but also to music, gaming, events, articles and more.
With the Crossing Minds item-based recommendation engine, we are capable of extracting item suggestions for each user based on memory and group data.
This gives us the capacity to calculate similarities between co-rated items and predict taste even with limited data.
Many companies lack the algorithms in place to prioritize a user’s taste or style in order to make future recommendations after interacting with the product. This is the case, as an example, for large online hotel or home booking sites, which require the user to start at zero each time they search.
These site search engines and filters aid users in completing the singular task of finding a place to stay in a particular city, but are in a deficit when it comes to having algorithms in place to collect user tastes or preferences during their search. In order to accurately recommend hotel rooms or homes that are styled or set-up similarly to places the user has booked before, these companies must find new ways to collect user preferences.
Better than the SOA
Recos per CPU per Hours
Lines of Code
We are very exited to share our newly released Recommendation API, which businesses can utilize to offer their customers the best recommendations possible.
If you are interested in being among the first to try it out, please answer the interest form. Our team will reach back to you as we are processing the flow of applications. Thanks!