# A Two-Phase Deep Learning-Based Recommender System: Enhanced by a Data Quality Inspector
###### Abstract  
Research regarding collaborative filtering recommenders has grown fast lately. However, little attention has been paid to discuss how the input data quality impacts the result. Indeed, some review-rating pairs that a user gave to an item are inconsistent and express a different opinion, making the recommendation result biased. To solve the above drawback, this study proposes a two-phase deep learning-based recommender system. Firstly, a sentiment predictor of textual reviews is created, serving as the quality inspector that cleans and improves the input for a recommender. To build accurate predictors, this phase tries and compares a set of deep learning-based algorithms. Secondly, besides only exploiting the consistent review-rating pairs generated by the quality inspector, this phase builds deep learning-based recommender engines. The experiments on a real-world dataset showed the proposed data quality inspector, based on textual reviews, improves the overall performance of recommenders. On average, applying deep learning-based quality inspectors result in an above 6% improvement in RMSE, and more than a 2% boost in F1 score, and accuracy. This is robust evidence to prove the importance of the input data cleaning process in this field. Moreover, empirical evidence indicates the deep learning approach is suitable for modeling the sentiment predictor, and the core recommendation process, clearly outperforming the traditional machine learning methods. A Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. [table]capposition=top  
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(c) the utilization of textual reviews, mouse clicks, triggered events, or eye-tracking as auxiliary information for personal interest verification, and (d) the recommendation accuracy as the main challenge to enhance [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. The up-to-date direction of researchers in a collaborative filtering strategy is to deal with the recommendation accuracy by focusing on problems correlated to it, such as sparsity or cold-start [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. Less attention has been paid to an essential problem that is highly correlated to accuracy: the quality of the ratings as input data. The authors of [15] use the votes of readers that denote if a review was helpful or not to compute a quality score. These quality scores are taken as weights assigned to the ratings in a probabilistic matrix factorization for performing the rating prediction. The authors of [16] investigated three ways of combining overall textual opinions with real ratings in a biased matrix factorization model. The results showed that the opinion post-filtering method, compared to the other two, and a standard algorithm based only on ratings, gives the best RMSE. The authors of [17] proposed a user-based collaborative filtering approach that considers explicit ratings and sentiment analysis extracted from users' reviews. Results from the collaborative filtering approach and sentiment analysis are combined to generate integrated ratings and create a list of recommendations. Although these researchers tried to improve the quality of the input, they only tuned the initial data, using as input a modified but complete version of the original ratings. The task where the input data is truly filtered or cleaned based on its quality was not conducted. Commonly, e-commerce product platforms allow users to give feedback to items through (a) a numeric rating and (b) a textual review. The main problem is that there are cases where these two are inconsistent or in conflict [16], as the rating from a user reflects a different opinion of his or her written review for the same item. These rating-review pairs can be categorized as low-quality or noisy input data, and although this issue has not been widely mentioned in the literature, plenty of these cases can be found on the web.