Introduction
Alie’s “Multiple data types” feature lets you add the “data” with multiple data types/structures to train the system for generating recommendations. All you need is to define the data type for each input parameter, allowing Alie to sync with the algorithm and train the data without any ambiguity.
A multi-domain recommendation system needs to handle a diverse set of data (e.g., ratings, views, likes, check-ins, etc.), while at the same time enabling easy integration of new types by modifying the underlying schema. This is where Alie offers a clear advantage by allowing you to add data with multiple data types.
Highlights
Highlights
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Remove data-type ambiguity
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Improved Accuracy
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Industry-specific recommendations
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Elevate User Experience with Personalized Recommendations
Remove data-type ambiguity
Remove Data-type Ambiguity
Data collected from multiple sources is usually available in an unorganized format, which can’t be ingested into machine learning(ML) systems directly. This is where the support for multiple data structures comes in handy. Alie supports all the data types which are used in ML systems. Whatever may be the application domain and dataset, Alie will ease out your job of entering the training data.
Improved Accuracy
Improved Accuracy
With support for Multiple Data Types, the accuracy of input data is drastically improved. Accurate data always lead to quality recommendations and a delightful customer experience.
Industry-specific recommendations
Industry-specific Recommendations
Today, consumer preferences are difficult to gauge. You should go beyond the hard data, such as transaction history, to comprehend customer sentiment, which could be found in - support chats, emails, and social posts. The support for multiple data types is useful here, as it helps in co-relating all the information into coherent input parameters to offer Intelligent Recommendations
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