JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2018, Vol. 48 ›› Issue (3): 17-24.doi: 10.6040/j.issn.1672-3961.0.2017.411

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Item embedding classification method for E-commerce

LONG Bai1, ZENG Xianyu1, LI Zhi1,2, LIU Qi1*   

  1. 1. Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230000, Anhui, China;
    2. School of Software Engineering, University of Science and Technology of China, Hefei 230000, Anhui, China
  • Received:2017-05-17 Online:2018-06-20 Published:2017-05-17

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Abstract: Inspired by the Word Embedding Model word2vec, which proved higly successful in the field of Natural Language Processing in recent years, two Item Embedding models item2vec and w-item2vec were proposed. By modeling users behaviour sequences, both item2vec and w-item2vec projected the items to distributed representations in vector space. The vectors of items represented the properties of items and could be used to measure the relations between items. By means of this property, we could categorize products effectively and efficiently. Experimental results showed that methods were conducted on a real-world dataset and w-item2vec achieved an accuracy of nearly 50% for item categorization by using only 10% of the items for training. Two proposed models outperformed other methods obviously.

Key words: item categorization, item emebedding, behavior modeling, word embedding, E-commerce

CLC Number: 

  • TP391
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