In today’s world where an individual is becoming more and more busy and independent, the use of recommendation-based systems is steadily increasing. Thus, making available professional knowledge to the common man in a short-span quite necessary. The aim of our recipe recommendation system is to recommend recipes to users based on their questions. To make the recommendation model important as well as meaningful, it is pertinent to display only those recommendations that have a greater probability to be fit for the asked question. We have addressed this challenge by working on a threshold parameter generated from the recommendation engine. Apart from this, we have also included a question classification (QC) task together with the question answering (QA) module. The QA module is used to extract the requisite answers from the recommended recipe based on the class label obtained from QC. The main contribution of this work is the proposal of a robust recommendation approach by enabling analysis of threshold estimation and proposal of a suitable dataset. The final output of the recommendation system obtains benchmark results on the human evaluation (HE) metric. Our code, pretrained models and the dataset will be made publicly available.

在个人变得越来越忙碌和独立的当今世界中，基于推荐的系统的使用正在稳步增加。因此，非常有必要在短时间内向普通人提供专业知识。我们的食谱推荐系统的目的是根据用户的问题向他们推荐食谱。为了使推荐模型既重要又有意义，应该只显示那些更有可能适合所提出问题的推荐。我们通过处理从推荐引擎生成的阈值参数来解决了这一挑战。除此之外，我们还包括了一个问题分类（QC）任务以及问题解答（QA）模块。QA模块用于根据从QC获得的类别标签从推荐的食谱中提取必要的答案。这项工作的主要贡献是通过支持阈值估计的分析和合适数据集的建议，提出了一种可靠的建议方法。推荐系统的最终输出获得有关人类评估（HE）指标的基准结果。我们的代码，预训练模型和数据集将公开提供。