# Multi-Feature Extension via Semi-Autoencoder for Personalized Recommendation
## 4 Methodology  
### Feature Representation Method Based on LSI Model  
In traditional feature representation learning algorithms, One-Hot coding is mostly applied to process discrete feature data. However, when there are numerous types of data, the generated vectors are highly sparse, which is called \"dimension explosion\" [38]. Vector mapping is an effective approach to address this problem, which has the capability of learning a low-dimensional continuous vector representation of items, and the mapping results for items with similar actual meanings in the vector space have similar distances. Vector mapping methods reduce noise and redundant information and preserve structural information between data. Common vector mapping methods include MF, random walk algorithms, and deep neural network models. Alternatively, vector mapping methods can use dense low-dimensional numerical feature vectors to represent high-dimensional sparse features [39]. This method also supports subsequent network processing tasks, including node classification [40], node clustering [41], and network visualization [42].  
LSA belongs to the topic model, which is constructed by SVD. It relies on a statistical analysis of a large set of texts to extract the semantics of words hidden in the context from the analyzed data. Figure 2 depicts the basic structure of the LSA model. First, the text dataset is analyzed and then the vocabulary text matrix corresponding to the text dataset is established. The resulting lexical text matrix is then decomposed into singular values with SVD. The process aims to map the vocabulary and text into the same vector space. The decomposed matrix is then dimensionally reduced and the processed low-dimensional data is used to construct the final latent semantic space. Compared to the traditional vector spaces, the semantic space constructed by the LSA principle has a smaller dimension, which effectively addresses the negative effects of synonyms and enhances the semantic relationship between words. LSA is a classic bag-of-words model and neglects word order. However, the main task of LSA in our method is to implement a vectorized mapping of the collected item features; the involved item features do not exist sequentially and do not affect the accuracy of the recommendation results.  
In our proposed method, we introduce the LSI model from Gensim ([[http://pypi.python.org/pypi/gensim](http://pypi.python.org/pypi/gensim)]([http://pypi.python.org/pypi/gensim](http://pypi.python.org/pypi/gensim)), accessed on 20 September 2022), which is a python library for automatically extracting semantic topics from documents to complete the vectorized mapping of features. LSI model first builds a dictionary according to the vocabulary data set and generates the corresponding bag of words model. It calculates the weight of words based on Term Frequency-Inverse Document Frequency (TF-IDF) [43]. TF-IDF refers to the larger the weight, the greater the importance of the word to the document. Finally, it reduces the dimensionality through the LSA algorithm to filter out word noise.  
The processed results are used as auxiliary information for the items, which effectively solves the sparsity problem in the personalized recommendation.