Recently, the application of Artificial Intelligence algorithms in hotel recommendation systems has become an increasingly popular topic. One such method that has proven to be effective in this field is Deep Learning, especially Natural Language processing models, which are able to extract semantic knowledge from user's text reviews to create more efficient recommendation systems. This can lead to the development of intelligent models that can classify a user's preferences and emotions based on their feedback in the form of text reviews about their hotel stay experience. In this study, we propose a Natural Language Processing framework that utilizes customer text reviews to provide personalized recommendations for the most appropriate hotel based on their preferences. The framework is based on Bidirectional Encoder Representations from Transformers (BERT) and a fine-tuning/validation pipeline that categorizes customer hotel review texts into "Bad," "Good," or "Excellent" recommended hotels. Our findings indicate that the hotel recommendation system we propose can significantly enhance the user experience of booking accommodations by providing personalized recommendations based on user preferences and previous booking history.

These platforms collect data about both users' and items' attributes, as well as accumulate the ratings and feedback of products and services, to develop algorithms for significant enhancement of users' experience on the marketplace. These algorithms are capable of influencing the purchasing behavior of users by (1) offering them the selection of the most relevant personalized positions, (2) reducing the individual searching costs, and (3) alleviating the information asymmetry on large commercial platforms with homogeneous sellers and products through feedback mechanisms. Since recommender systems have the power to affect the marketing decisions of users, they have become an attractive target for ratings and reviews manipulations, also known as attacks. Specifically, these attacks are aimed at inflating/deflating the ranks and text reviews of certain product positions or at simply sabotaging the efficiency and credibility of the the commercial platform in general. The current study focuses on solving the task of filtering out the deceptive opinions and detecting anomalous behavior on a platform with text reviews. The emphasis on text reviews can be explained by the fact that texts are a more informative and a more reliable source of product's and seller's quality, than a star-rating system, which is easy to manipulate (see [19], [14], [27], [28]).

Collaborative Filtering(CF) recommender is a crucial application in the online market and ecommerce. However, CF recommender has been proven to suffer from persistent problems related to sparsity of the user rating that will further lead to a cold-start issue. Existing methods address the data sparsity issue by applying token-level sentiment analysis that translate text review into sentiment scores as a complement of the user rating. In this paper, we attempt to optimize the sentiment analysis with advanced NLP models including BERT and RoBERTa, and experiment on whether the CF recommender has been further enhanced. We build the recommenders on the Amazon US Reviews dataset, and tune the pretrained BERT and RoBERTa with the traditional fine-tuned paradigm as well as the new prompt-based learning paradigm. Experimental result shows that the recommender enhanced with the sentiment ratings predicted by the fine-tuned RoBERTa has the best performance, and achieved 30.7% overall gain by comparing MAP, NDCG and precision at K to the baseline recommender. Prompt-based learning paradigm, although superior to traditional fine-tune paradigm in pure sentiment analysis, fail to further improve the CF recommender.

In my previous article, I've been analyzing text reviews of women clothing online purchases in order to extrapolate the sentiment of customers. The idea was to investigate whether the sentiment was consistent with the purchase recommendation. In this article, I'm going to keep analyzing the text review, but now focusing on the rating column, which indicates a score from 1 (worst) to 5 (best) attributed to the item. Let's refresh which is the dataset we are talking about (you can find it on Kaggle): For this purpose, we are going to build a Neural Network using Keras (with Tensorflow embedded). Before starting, we need to do some further preprocessing of our dataset.

Natural Language Processing (NPL) is a field of Artificial Intelligence whose purpose is finding computational methods to interpret human language as it is spoken or written. The idea of NLP goes beyond a mere classification task that could be carried on by ML algorithms or Deep Learning NNs. Indeed, NLP is about interpretation: you want to train your model not only to detect frequent words, but also to count them or to eliminate some noisy punctuations; you want it to tell you whether the mood of the conversation is positive or negative, whether the content of an e-mail is mere publicity or something important, whether the reviews about thriller books in last years have been good or bad. The good news is that, for NLP, we are provided with interesting libraries, available in Python, that offer a pre-trained model able to inquire about written text. Among those, I'm gonna be using Spacy and NLTK.