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Resource-constrained 020 service recommended strategy research

机译：资源约束020服务推荐策略研究

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摘要

With the development of internet, the problem of information overload is becoming more and more serious. Recommendation technology can screen out information that is useful to people. Therefore, many scholars pay attention to it. There are two types of internet services: online and offline. At present, recommendation technology has become more and more mature in purely online applications, including news recommendation and commodity recommendation. However, O2O service recommendation needs the support of offline resources. Owing to the constraints of limited resources, many users adopt recommendation result at the same time, which often leads to crowded service points, useless recommendations and poor user experience. How to improve the effectiveness of O2O service recommendation under condition of resource constraints is a crucial issue. This paper proposes a group of O2O service recommendation strategies from the prospect of supply and demand matching to solve the problem step by step. Furthermore, we utilise computational experiment to perform performance comparison analysis for these service strategies. The results show that the adaptive adjustment mechanism based on current supply and demand conditions is conductive to improving effectiveness of O2O service recommendation so as to increase profit of the merchant and improve user experience.
机译：随着互联网的发展，信息过载问题变得越来越严重。推荐技术可以筛选对人有用的信息。因此，很多学者都会关注它。有两种类型的互联网服务：在线和离线。目前，推荐技术在纯粹在线申请中变得越来越成熟，包括新闻推荐和商品推荐。但是，O2O服务推荐需要支持离线资源。由于资源有限的限制，许多用户同时采用推荐结果，这通常会导致拥挤的服务点，无用的建议和用户体验不佳。如何在资源限制条件下提高O2O服务推荐的有效性是一个至关重要的问题。本文提出了一组O2O服务推荐策略，从供应和需求匹配的前景逐步解决问题。此外，我们利用计算实验来对这些服务策略进行性能比较分析。结果表明，基于当前供需条件的自适应调整机制是提高O2O服务推荐的有效性，以提高商家的利润，提高用户体验。

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