## 2 Materials and Methods
### Neural Language Model  
Data-driven decisions are becoming increasingly important, and natural language processing (NLP) is playing an increasingly vital role. NLP is the process of extracting meaningful information from text. Its application to tasks such as sentiment analysis, information retrieval, language detection, text summarization, and many others has made it an invaluable tool for making decisions based on large amounts of unstructured data. The specific objectives of many NLP applications can all be generalized to calculating the probability of a given sequence of words [45; 46].  
In NLP, words are the basic unit of meaning and word vectors are used to represent them. The process of mapping words to real vectors is called word embedding. One-hot encoding is a common approach for language modeling, but it can be problematic as it results in a loss of meaning for words [47]. Representing words as vectors, using Word2 vec, captures the semantic relationship between words and the context in which they are used. This allows models to better understand the meaning of words and to consider the context in which they are used. Additionally, representing words as vectors also helps reduce the number of features needed in a model [14; 15].  
Over the past few years, distributed representation has become a popular tool for representing words and phrases in NLP applications. Most of the work in the Word2 vecarea is based on the distributional hypothesis [48; 49], which holds that words that appear in the same context have similar meanings. This assumes that contexts are influential in shaping a word's meaning. However, research has recently extended the use of distributed representation beyond word representation to other types of data. For example, distributed representation can be used to represent images and audio clips [50]. Table 2 highlights some of the versatility of word embedding usage in various domains.  
From a recommendation perspective, item representation plays a crucial role in the effectiveness of a recommendation system. Systems employing more precise and diverse representations of items are likely to yield more accurate recommendations. Numerous techniques have been proposed for representing items in a manner that takes into account user transactions and item content information, including review text and images. Some notable studies in this domain [61] include the use of co-purchase data to target and personalize ads in the Yahoo Mail advertising system; [PERSON] et al. [62] proposed a method to leverage the existing metadata of items to make better recommendations; and [55] introduces an approach for constructing product embeddings that uses several modality-specific features, such as text description and images.  
In the context of constructing a machine learning model, the capacity to enhance its performance is key. The calibration of the model's hyperparameters can yield a considerable influence on its precision. Default hyperparameters are employed to streamline a model for a particular set of problems or data, frequently being selected by the machine learning algorithm or adjusted by the model developer. However, when the problem domain extends beyond the initial scope, the circumstances may deviate. For example, [PERSON] et al. [63] implement the skip-gram with a negative sampling technique, a variant of Word2 Vec, to generate item embeddings for recommendation systems. The authors scrutinize the significance of hyperparameters in a recommendation context and ascertain that the optimization of previously disregarded hyperparameters can markedly enhance performance. Additionally, they discover that the optimal hyperparameters for natural language processing and recommendation tasks differ. This methodology has been employed to produce item embeddings that have proven successful in the recommendation domain, yielding substantially improved performance and elevated recommendation accuracy. In this study, we have also adopted a similar conjecture to fine-tune the change propagation setting.