  Intelligent recommendation and reminder systems are the need of the
fast-pacing life. Current intelligent systems such as Siri, Google Assistant,
Microsoft Cortona, etc., have limited capability. For example, if you want to
wake up at 6 am because you have an upcoming trip, you have to set the alarm
manually. Besides, these systems do not recommend or remind what else to carry,
such as carrying an umbrella during a likely rain. The present work proposes a
system that takes an email as input and returns a recommendation-cumreminder
list. As a first step, we parse the emails, recognize the entities using named
entity recognition (NER). In the second step, information retrieval over the
web is done to identify nearby places, climatic conditions, etc. Imperative
sentences from the reviews of all places are extracted and passed to the object
extraction module. The main challenge lies in extracting the objects (items) of
interest from the review. To solve it, a modified Machine Reading
Comprehension-NER (MRC-NER) model is trained to tag objects of interest by
formulating annotation rules as a query. The objects so found are recommended
to the user one day in advance. The final reminder list of objects is pruned by
our proposed model for tracking objects kept during the "packing activity."
Eventually, when the user leaves for the event/trip, an alert is sent
containing the reminding list items. Our approach achieves superior performance
compared to several baselines by as much as 30% on recall and 10% on precision.
