# Leveraging User Comments for Recommendation in E-Commerce
## 1 Introduction  
Recommender systems [1; 2; 3] are able to analyze the past behavior of customers and recommend the products in which they might be interested. Recommender systems can roughly be categorized into two types: collaborative filtering and content-based filtering. Content-based filtering [4; 5; 6] assumes that customers will buy things that are similar to what they have bought in the past. Therefore, detailed information about products and users are required for recommendations. However, the information needed is growing harder to get in the modern age of privacy awareness. Collaborative filtering [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17] assumes that similar users have similar interest in items and similar items have similar ratings by users. Traditionally, users give their preference ratings for the previously purchased products, and these ratings are maintained in a user-item rating matrix. Based on the content of the matrix, collaborative filtering makes recommendations to a user according to the opinions of other like-minded users on the products. In general, collaborative filtering is simpler and more practical, and it tends to be more appealing in the E-commerce community.  
However, there exist some issues with collaborative filtering. Two of them are data sparsity and scalability [18; 19; 20]. Data sparsity is related to the sparse ratings in the user-item rating matrix, and it can lead to inaccurate recommendations. Scalability is related to the huge number of products or/andusers involved, which may cause an unacceptably long delay before valuable recommendations are acquired. In this paper, we propose a novel approach to deal with data sparsity and scalability. First of all, we apply Word2 Vec [21; 22], which is a word semantic tool developed by Google, to analyze the user comments on their previously bought goods. Each word is assigned a unique vector that represents its semantics. A set of item vectors, one item vector for each product, is then developed. Through the user-item rating matrix, the user vectors of all the users are obtained. Principal component analysis and an iterative self-constructing clustering algorithm are applied to reduce the time complexity related to the large numbers of items and users. Then, recommendation work is done with the resulting clusters. Finally, reverse transformation is performed, and a ranked list of recommended items can be offered to each user. With the proposed approach, the inaccuracy caused by the sparse ratings in the user-item rating matrix is overcome, and the processing time for making recommendations from an enormous amount of data is much reduced.  
The proposed approach is an extension of our previous work published in [23]. Some major advantages over the previous work are provided. Word2 Vec is applied on user comments to find the similarity relationship among products. Therefore, the sparsity problem is alleviated, and the accuracy of recommendation is improved. An iterative self-constructing clustering algorithm is used. The clusters obtained are less dependent on the order of the training patterns presented to the clustering algorithm, leading to more robust recommended results. Finally, clustering is applied not only on the products but also on the users. Consequently, the scalability problem can be further alleviated.  
The rest of this paper is organized as follows. In Section 2, collaborative filtering, particularly our previous work [23], is briefly introduced. Some other related work is reviewed and discussed in Section 3. The proposed approach is described in detail in Section 4. Experimental results are presented in Section 5. Finally, a conclusion is given in Section 6.