使用成分网络的配方推荐
Recipe recommendation using ingredient networks
摘要：
烹饪食谱的记录和分享，一项可以追溯到数千年前的人类活动，自然成为网络的早期和突出的社交用途。由此产生的在线食谱集合是成分组合和烹饪方法的存储库，其大规模和多样性产生了关于烹饪基础和用户偏好的有趣见解。在单个成分的水平上，我们衡量它是否是必不可少的，或者可以删除或添加，以及它的数量是否可以修改。我们还构建了两种类型的网络来捕捉成分之间的关系。补体网络捕获哪些成分往往经常共存，并由两个大社区组成：一个是咸味，另一个是甜味。从用户生成的修改建议中导出的替代网络可以分解为功能等同成分的许多社区，并捕获用户对食谱的更健康变体的偏好。我们的实验表明，配方评级可以很好地预测来自成分网络和营养信息的组合的功能。
英文摘要：
The recording and sharing of cooking recipes, a human activity dating back thousands of years, naturally became an early and prominent social use of the web. The resulting online recipe collections are repositories of ingredient combinations and cooking methods whose large-scale and variety yield interesting insights about both the fundamentals of cooking and user preferences. At the level of an individual ingredient we measure whether it tends to be essential or can be dropped or added, and whether its quantity can be modified. We also construct two types of networks to capture the relationships between ingredients. The complement network captures which ingredients tend to co-occur frequently, and is composed of two large communities: one savory, the other sweet. The substitute network, derived from user-generated suggestions for modifications, can be decomposed into many communities of functionally equivalent ingredients, and captures users' preference for healthier variants of a recipe. Our experiments reveal that recipe ratings can be well predicted with features derived from combinations of ingredient networks and nutrition information.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
