Abstract: Recently, online marketplaces in the e-commerce industry, such as Rakuten and Alibaba, have become some of the most popular online marketplaces in Asia. In these shopping websites, consumers can select purchase products from a large number of stores. Additionally, consumers of the e-commerce site have to register their name, age, gender, and other information in advance, to access their registered account. Therefore, establishing a method for analyzing consumer preferences from both the store and the product side is required. This study uses the Doc2Vec method, which has been studied in the field of natural language processing. Doc2Vec has been used in many cases to analyze the extraction of semantic relationships between documents (represented as consumers) and words (represented as products) in the field of document classification. This concept is applicable to represent the relationship between users and items; however, the problem is that one more factor (i.e., shops) needs to be considered in Doc2Vec. More precisely, a method for analyzing the relationship between consumers, stores, and products is required. The purpose of our study is to combine the analysis of the Doc2vec model for users and shops, and for users and items in the same feature space. This method enables the calculation of similar shops and items for each user. In this study, we derive the real data analysis accumulated in the online marketplace and demonstrate the efficiency of the proposal.
Abstract: This paper proposes an auto-classification algorithm
of Web pages using Data mining techniques. We consider the
problem of discovering association rules between terms in a set of
Web pages belonging to a category in a search engine database, and
present an auto-classification algorithm for solving this problem that
are fundamentally based on Apriori algorithm. The proposed
technique has two phases. The first phase is a training phase where
human experts determines the categories of different Web pages, and
the supervised Data mining algorithm will combine these categories
with appropriate weighted index terms according to the highest
supported rules among the most frequent words. The second phase is
the categorization phase where a web crawler will crawl through the
World Wide Web to build a database categorized according to the
result of the data mining approach. This database contains URLs and
their categories.