As an essential product cue in consumer information foraging, textual snippets can convey valuable scents that attract consumers to further access products, paving the way for online sellers to seize a competitive advantage. Premised on shopping goals theory, this study proposes a novel approach to designing high-quality product snippets that are particularly enhanced with consumer interests. First, snippet encoding is formulated as a multi-criteria optimization problem in which information sources are incorporated to distill consumer-appealing keywords considering both driving search and attracting selection, as well as to integrate the perspectives of sellers and consumers. Subsequently, a constrained genetic solution algorithm is developed, which copes well with the evolutionary nature of the problem to optimize snippets in an effective and efficient manner. Extensive experiments are conducted to verify the validity and superiority of the proposed approach.

中文翻译：

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使用多标准遗传优化方法将消费者兴趣编码到产品片段中


作为消费者信息搜寻中必不可少的产品线索，文本片段可以传达有价值的气味，吸引消费者进一步访问产品，为在线卖家抓住竞争优势铺平道路。以购物目标理论为前提，本研究提出了一种新颖的方法来设计高质量的产品片段，这些片段特别增强了消费者的兴趣。首先，片段编码被表述为一个多标准优化问题，其中信息源被整合以提炼出吸引消费者的关键词，同时考虑推动搜索和吸引选择，以及整合卖家和消费者的观点。随后，开发了一种约束遗传解算法，该算法很好地应对了问题的进化性质，以有效和高效的方式优化片段。进行了广泛的实验，以验证所提方法的有效性和优越性。