Session-Based Recommendations Using Item Embedding

Asnat Greenstein-Messica, L. Rokach, Michael Friedmann
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引用次数: 38

Abstract

Recent methods for learning vector space representations of words, word embedding, such as GloVe and Word2Vec have succeeded in capturing fine-grained semantic and syntactic regularities. We analyzed the effectiveness of these methods for e-commerce recommender systems by transferring the sequence of items generated by users' browsing journey in an e-commerce website into a sentence of words. We examined the prediction of fine-grained item similarity (such as item most similar to iPhone 6 64GB smart phone) and item analogy (such as iPhone 5 is to iPhone 6 as Samsung S5 is to Samsung S6) using real life users' browsing history of an online European department store. Our results reveal that such methods outperform related models such as singular value decomposition (SVD) with respect to item similarity and analogy tasks across different product categories. Furthermore, these methods produce a highly condensed item vector space representation, item embedding, with behavioral meaning sub-structure. These vectors can be used as features in a variety of recommender system applications. In particular, we used these vectors as features in a neural network based models for anonymous user recommendation based on session's first few clicks. It is found that recurrent neural network that preserves the order of user's clicks outperforms standard neural network, item-to-item similarity and SVD (recall@10 value of 42% based on first three clicks) for this task.
使用项目嵌入的基于会话的推荐
最近用于学习词的向量空间表示、词嵌入的方法,如GloVe和Word2Vec,已经成功地捕获了细粒度的语义和句法规律。我们通过将用户在电子商务网站浏览过程中产生的商品序列转换成一个单词句子来分析这些方法在电子商务推荐系统中的有效性。我们使用现实生活中用户在一家欧洲在线百货商店的浏览历史,研究了细粒度商品相似性(例如最类似于iPhone 6 64GB智能手机的商品)和商品相似性(例如iPhone 5与iPhone 6之比就像三星S5与三星S6之比)的预测。我们的研究结果表明,这些方法在不同产品类别的项目相似性和类比任务方面优于相关模型,如奇异值分解(SVD)。此外,这些方法产生了一个高度浓缩的项目向量空间表示,即具有行为意义子结构的项目嵌入。这些向量可以用作各种推荐系统应用程序的特征。特别是,我们使用这些向量作为基于神经网络的模型的特征,基于会话的前几次点击进行匿名用户推荐。研究发现,保留用户点击顺序的递归神经网络在此任务中优于标准神经网络、物品到物品相似性和SVD (recall@10基于前三次点击的值为42%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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