# Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering
## 3 Related Works  
Currently, information generated by users is being explored and linked to recommendations either by expanding the filtering method or as an alternative method to solve the problem of data scarcity. For example, in [6], long short-term memory (LSTM) is used to extract relevant aspects of user reviews. The authors of that study define three types of sentences, i.e., those that contain context words, those that contain words associated with the characteristics of the item, and those that contain polarization words (good, bad, etc.). Mutual information mechanisms are used to integrate the different classes into a single, final opinion. Gradient reinforcement learning is used to minimize errors. A framework based on deep learning is proposed in [7] to generate explanations of recommendations from reviews about target items. The proposal was validated with data from Amazon.com. The incorporation of opinions in the recommendation system, as a means of providing additional feedback, is proposed in [8]. A comparison of various collaborative filtering methods demonstrated that the value decomposition (SVD), non-negative matrix factorization (NMF), and SVD ++, used with a combination of explicit user ratings as well as ratings derived from opinions, improves the effectiveness of recommender systems. In [9], a methodology to obtain explanations of recommended items is proposed. The most relevant aspects discussed in the opinions are obtained. Then, positive sentences associated with those aspects are retrieved. Finally, a template is filled out with positive information, which is then presented to users as a justification of why they should obtain the item in question. Natural language processing techniques are used. In [10], the authors analyzed opinions to generate explanations of recommendations using a multilevel dynamic programming algorithm called Deep Explicit Attentive Multi-View Learning (DEAML), in addition to predicting the ratings from opinions. A recurrent neural network to generate explanations of recommendations from textual and visual information is used in [11]. The authors of that study presented which ever aspect of an image they focused on to make recommendations. The authors in [12] proposed a multiple view recurrent neural network model for sequential recommendations in which a matrix representing the user's ratings is multidimensional, containing information from different sources, including user opinions. In [13], a thoughtful, aspect-based recommendation model that effectively captures the interactions between aspects extracted from reviews is presented. In recent years, popular services such as Netflix have applied matrix factorization methods to make recommendations. Matrix factorization has been shown to be effective for processing large amounts of information, but ascarcity of data makes it deficient [14; 15]. Explicit user feedback to predict ratings is increasingly difficult to obtain. Works related to obtaining implicit feedback from users have emerged as a solution to this problem [16]. Generally, the text is transformed into a numerical value to be treated as an explicit rating in collaborative filtering matrices. This action generates uncertainty and error in the transformation of a large amount of text to a single numerical value [17]. A matrix factorization method that uses implicit user information was proposed in [18]. The implicit information comes from a list of products that have been viewed by a user, who went on to view a target product. In [19], an implicit rating value is attributed to the usefulness of a given opinion; the authors of that study propose a neural attentional regression model to predict more precise ratings. In [20], rating sare generated by combining explicit and implicit feedback, and a probabilistic matrix factoring model is used to predict that rating. Explicit feedback is when a user rates an item, and when other users indicate that that the opinion was useful. The search history is considered implicit feedback.