### Related Work  
Incorporating the latent factors associated with users has been proven to be very useful to design effective algorithms in ranking and recommendation systems [8; 28; 29]. These works are based on the idea of factorizing a matrix to linearly reduce the dimensionality of the problem and extract some commonalities that may be implicit in the data under analysis. Most of the techniques used the Singular Value Decomposition (SVD) to perform the matrix decomposition or its high-order SVD version for tensors. One common problem of this type of solution is missing data that may be solve using the average ratings for an item. However, this solution is only possible if second step is adopted to learn the association between the new created features and the ranking (e.g., using item-item or user-user collaborative filtering approach individually). Another approach is to use Alternative Least Squares [30] to minimize the difference between the original data and the projected that consider only non-missing values. Moreover, the large sparsity of the data under analysis can be up to 99% for datasets such as Amazon product review undermining the capabilities of this type of learning methods based on matrix decomposition. Some alternative works propose the use of specific regularization parameters to minimize this effect [28].  
In our field of recommendation explanation, the sparsity is increased because we not only use the rating value for each product but the opinionated dictionary for each one. It has been demonstrated that this system's ability to explain why items are suggested and a good explanation interface could help inspire user trust and satisfaction [31; 32]. This human-centered approach distinguishes from raw sentiment analysis by using the second as input for the recommendation system. Thereof the system explains why the products are recommended by using the sentiment/opinion-words and provides transparency, sense of control of the system, and improves user overall satisfaction [33].  
In this context, we postulate that given the largeness and sparsity training dataset, CF techniques should be used to predict a set of sentiment strength scores associated with a list of opinion keywords that essentially compose the user's opinion on an item he did not review yet. Reviews contain opinions, topical and emotional information that gives clues about what the writer possibly wants to transmit [34]. The first studies distinguished between positive (favorable) and negative (unfavorable) opinions about a topic [35; 36], and the extracted sentiments were later used to infer unknown ratings [22; 37]. However, none of these works aimed to predict the texts reviews themselves, nor the sentiments of the lists of opinion keywords that characterize them. Moreover, we assume that this user's opinion may be described as a set of features in a so-called latent space. There are previous works that support this assumption [11] indicating that there exist a latent space that represents the user interest and tastes although the authors did not provide a model to predict the sentiment score of the item characteristics.  
The rest of the article is organized as follows: Section 2 contains the description of the opinion prediction model and our overall approach. Section 3 describes the experiments we conducted to test the implemented model, and Section 4 includes the obtained results. Finally, Section 5 presents the conclusions and some insights into future work.