Deep Context-Aware Recommender System Utilizing Sequential Latent Context
This work addresses the problem of high dimensionality and sparsity in context-aware recommender systems for users needing personalized services, representing an incremental improvement through a novel method.
The authors tackled the challenge of incorporating sequential context into recommender systems by proposing a new latent modeling approach using LSTM encoder-decoder networks to compress contextual sequences, resulting in a model that surpasses state-of-the-art context-aware recommender systems on two datasets.
Context-aware recommender systems (CARSs) apply sensing and analysis of user context in order to provide personalized services. Adding context to a recommendation model is challenging, since the addition of context may increases both the dimensionality and sparsity of the model. Recent research has shown that modeling contextual information as a latent vector may address the sparsity and dimensionality challenges. We suggest a new latent modeling of sequential context by generating sequences of contextual information and reducing their contextual space to a compressed latent space.We train a long short-term memory (LSTM) encoder-decoder network on sequences of contextual information and extract sequential latent context from the hidden layer of the network in order to represent a compressed representation of sequential data. We propose new context-aware recommendation models that extend the neural collaborative filtering approach and learn nonlinear interactions between latent features of users, items, and contexts which take into account the sequential latent context representation as part of the recommendation process. We deployed our approach using two context-aware datasets with different context dimensions. Empirical analysis of our results validates that our proposed sequential latent context-aware model (SLCM), surpasses state of the art CARS models.