DSER: Deep-Sequential Embedding for single domain Recommendation

Recently, Deep Neural Networks (DNNs) have proved their capability to model nonlinear relationships between users and items in recommender systems. Therefore, many researchers have exploited the DNN to model the non-linearity of individual interactions between users and items. However, it has been barely studied to model the non-linearity of a user's sequential item interactions that may be the fundamental clue to represent a user's interest naturally. This paper proposes a novel Deep Sequential Embedding for a single domain Recommendation based on implicit feedback, named DSER. It exploits Doc2vec techniques to convert sequential user–item interactions, rather than textual information, to the feature vectors of users and items. In our framework, a user's interaction is like a document containing items as words in the word embedding techniques. The obtained user and item vectors are then fed to Multi-Layer Perceptron (MLP) to model a high level of non-linearity for user–item interactions. Finally, the non-linearity of MLP is fused with the linear user–item relationships trained by matrix factorization to predict the possibility that a target user likes items. Extensive experiments on four real-world datasets demonstrate significant improvements of our DSER over state-of-the-art approaches. Moreover, empirical evidence shows that using word embedding offers considerably better predictive performance on sparse data of the smart tourism field, whose new user ratio is relatively high.