# Re-Enrichment Learning: Metadata Saliency for the Evolutive Personalization of a Recommender System
## 2 Background  
Recommender systems can be largely classified into three categories: the collaborative approach, content-based approach, and hybrid approach [16; 17; 25]. Firstly, the collaborative approach is based on the idea that people with similar preferences are likely to agree on an evaluation of an item. This approach consists of neighborhood-based methods, which directly use the users' item ratings to estimate the classification of a new item, and model-based methods, which use the ratings to acquire knowledge and teach a predictive model. The advantage of this approach is that new data can easily be added in an incremental manner, whereas its weakness to the cold-start problem of new users and the sparsity problem are its disadvantages. [PERSON] et al. (SVD) [26] suggested a singular value decomposition (SVD)-based matrix factorization algorithm that uses stochastic gradient descent for optimization. [PERSON] et al. (Bayesian personalized ranking (BPR)) [27] introduced a generic optimization criterion and learning algorithm for personalizing a recommender system. The optimization criterion is based on the maximum posterior estimator derived from Bayesian analysis, while the learning algorithm is based on stochastic gradient descent with bootstrap sampling. [PERSON] et al. (L-GCN) [28] adopted the neighborhood aggregation of a graph convolution network (GCN) [29] and proposed learning user and item embeddings by linearly propagating them with respect to the interaction graph of the user and item. They used the weighted summation of the embeddings, which are learned at all layers, as in the final stage. Then, the content-based approach is based on the combination of a user's profile and an item description. In other words, the similarity between the items that the user has liked in the past and the detailed content of an item play important roles. This approach shows strength when recommending an item that is not yet rated by any user, whereas it suffers from the overspecialization problem. [PERSON] et al. (deep knowledge-aware network (DKN)) [30] proposed a multi-channel and word-entity-aligned knowledge-aware convolutional neural network for semantic and knowledge-based representation of news as well as an attention module to dynamically compute the aggregated historical representation of a user. [PERSON] et al. (neural news recommendation with personalized attention (NPA)) [31] suggested personalized convolutional neural networks that adopt the embedding of user IDs as the queries of the correlations between words and news. The meta-context dimension tree (Meta CDT) was proposed for the selection of the most suitable contents and services for a user in a certain context, and was used in a practical context-aware application by [PERSON] et al. [32] and [PERSON] et al. [33]. The approach has strength in tailoring the information domain according to the user's needs as well as in analyzing relevant features of context models. Lastly, there also are hybrid approaches that combine the collaborative and content-based approaches to exploit the advantages of the individual approaches [16; 34]. Many types have been proposed, such as by aggregating the predictions of collaborative and content-based approaches or integrating one approach's characteristics into the other. [PERSON] (light factorization machine (L-FM)) [35] proposed a hybrid matrix factorization model that represents users and items as linear combinations of the latent factors of their content features. [PERSON] et al. (wide and deep (W&D)) [36] introduced a framework for jointly training feed-forward neural networks with embedding and a linear model based on feature transformation for generic recommenders with sparse inputs. Many of the recent studies have discussed the problem that unbalanced or biased recommendations can shrink the diversity of interests of a user. Practical evaluation scenarios and techniques have been introduced to produce unbiased estimators in spite of biased or missing data [37; 38].