An Intelligent Recommendation-cum-Reminder System

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 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.