An Intelligent Recommendation-cum-Reminder System
Rohan Saxena, Maheep Chaudhary, Chandresh Kumar Maurya, Shitala Prasad

TL;DR
This paper introduces an intelligent system that processes emails to generate personalized recommendation and reminder lists for users, utilizing advanced entity recognition and web information retrieval to enhance packing and preparation for trips.
Contribution
The work presents a novel integrated approach combining email parsing, NER, web data retrieval, and a modified MRC-NER model for object extraction, improving reminder accuracy.
Findings
Achieves up to 30% higher recall over baselines
Improves precision by 10% compared to existing methods
Successfully recommends packing items one day in advance
Abstract
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…
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Taxonomy
MethodsAttention Model
