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首页> 外文期刊>Journal of digital information management >Do Online Recommendations Matter? - A Multimodal Investigation of Amazon's Co-Purchase Network
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Do Online Recommendations Matter? - A Multimodal Investigation of Amazon's Co-Purchase Network

机译：在线推荐很重要吗？ -亚马逊共同购买网络的多模式调查

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摘要

Online recommendations are considered as a useful approach to encourage consumer purchases and drive product sales in e-commerce. This paper uses a multimodal approach to examine the effectiveness of online recommendations using the lenses of econometric modeling and text mining. Applying data on Amazon's "customers who bought this item also bought" recommendation, commonly referred to as the co-purchase network, this study presents several findings. First, the effects of direct co-purchase recommendations on product sales are essentially strong, regardless of other potential factors that can also affect those sales. Second, the sales of one product can have positive effect on the sales of another, when the latter is suggested in co-purchase recommendations. Moreover, the similarity of the customer base between the two co-purchased products, which can be extracted using text mining from online reviews, will affect that product's sales. This finding leads to the conclusion that online recommendations achieve by recommending related products to like-minded consumers, and that recommendation effect is significant. These results contribute to academics and practitioners by providing clear economic evidence of the value of using text mining approaches for online recommendations.
机译：在线推荐被认为是鼓励消费者购买和推动电子商务中产品销售的有用方法。本文采用多模式方法，通过计量经济学建模和文本挖掘的角度来检验在线推荐的有效性。应用有关亚马逊“购买此商品的顾客也购买”建议的数据（通常称为共同购买网络），该研究提出了一些发现。首先，直接购买共同推荐对产品销售的影响实质上是很强的，而不管其他可能影响这些销售的潜在因素如何。其次，当在共同购买建议中建议使用另一种产品时，一种产品的销售会对另一种产品的销售产生积极影响。此外，两个共同购买产品之间的客户基础相似性（可以使用文本挖掘从在线评论中提取出来）将影响该产品的销售。这一发现得出的结论是，在线推荐是通过向志趣相投的消费者推荐相关产品来实现的，并且推荐效果显着。这些结果为使用文本挖掘方法进行在线推荐的价值提供了明确的经济证据，从而为学术界和从业者做出了贡献。

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