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Found 4 papers, 2 papers with code
Recommending Target Actions Outside Sessions in the Data-poor Insurance Domain
Our models cope with data scarcity by learning from multiple sessions and different types of user actions.
Graph-based Recommendation for Sparse and Heterogeneous User Interactions
We propose a graph-based recommender model which utilizes heterogeneous interactions between users and content of different types and is able to operate well on small-scale datasets.
Learning Recommendations from User Actions in the Item-poor Insurance Domain
To address this, we present a recurrent neural network recommendation model that uses past user sessions as signals for learning recommendations.
User-click Modelling for Predicting Purchase Intent
We examine the discriminativeness of user-clicks for predicting purchase intent by comparing the above two models with a model using demographic features of the user.
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