%0 Journal Article %T 电商商品嵌入表示分类方法<br>Item embedding classification method for E-commerce %A 龙柏 %A 曾宪宇 %A 李徵 %A 刘淇< %A br> %A LONG Bai %A ZENG Xianyu %A LI Zhi %A LIU Qi %J 山东大学学报（工学版） %D 2018 %R 10.6040/j.issn.1672-3961.0.2017.411 %X 摘要： 借鉴近些年来在自然语言处理领域卓有成效的一种词嵌入模型word2vec,提出两种商品嵌入表示模型item2vec和w-item2vec。提出的两种模型通过对用户在每次购买时对商品的比较和选择行为进行建模,将商品表示为一个低维空间的向量,该向量可以有效地对不同商品之间的关系和性质进行度量。应用这一性质,使用item2vec和w-item2vec得到的向量对商品进行分类,试验结果表明:在仅使用10%数据训练的基础上,w-item2vec对商品分类的准确率可以接近50%。两种模型分类准确性均显著优于其他模型。<br>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 %K 电子商务 %K 商品分类 %K 词嵌入 %K 行为建模 %K 商品嵌入 %K < %K br> %K item categorization %K item emebedding %K behavior modeling %K word embedding %K E-commerce %U http://gxbwk.njournal.sdu.edu.cn/CN/10.6040/j.issn.1672-3961.0.2017.411