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What Makes Segmentino Unique
- Collaborative Filtering
- Deep Learning
- Specialized Models
- Image Processing
- Model Optimization
Models based on behavioral patterns
The first type of the models Segmentino adds to its model ensembles are Collaborative Filtering models, which are built from collected user-item interactions, such as detail-views or purchases. By analyzing behavioral patterns across the whole user base, the recommendations are based on extracting interactions similarities between users, items, or both. “Similar users also liked” or “others also purchased” are both examples of CF-based recommendations. Segmentino uses following CF models: Matrix Factorization, Nearest Neighbor methods, and Association Rules.
Models based on attributes of items and users
We also use Content-Based models, which estimate similarities between items or users by analyzing the provided property values. For example, two items can be considered similar by having similar categorization, name, text descriptions, etc. Various models are used to process a different type of attribute data. These models are especially useful in cold – start situations when there’s not enough interaction data yet(brand new item or user).
Models combining interaction and attribute data together
Following the cutting-edge research in the field, Segmentino offers models based on neural networks and deep autoencoders to build the recommendations. Such models are able to consider at once all the data provided in the given context. The models build AI-based understanding of concepts hidden in the data.
Models reflecting specific business-cases and product needs
Diversification models (recommending variety of different items), popularity-based models (long-term or trending), reminder models or models periodicity-based models (based on repeating behavior in user-item interactions), are also part of Segmentino. These come from vast amount of experience that Segmentino team gained during years in business, applying the systems to hundreds of different use-cases.
Models based on analyzing images and visual similarity
Segmentino can process product images to extract similarities based on visual style. This allows e.g. recommending items which are visually similar to those liked by a user in the past. Advanced models based on top of convolutional neural networks are used for that. Multiple images (such as photos taken from different angles) can be provided to further improve the performance.
AI-Based Model Optimization
Automated searching for proper hyperparametrization
The models in production need to continuously adapt to the changes in the environment such as different seasons or holidays. When put to production, the models adapt based on collected feedback. Knowing whether the recommendations really led to user actions allows Segmentino to tune both the structure and the hyperparameters of the deployed model ensemble.
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