A novel recommendation system to match college events and groups to students

TL;DR

This paper introduces a text-content-based recommendation system for matching students with college events and groups, utilizing keyword expansion and modified tf-idf to improve accuracy over traditional methods.

Contribution

The paper presents a novel approach to user and item modeling using keyword expansion and modified tf-idf, enhancing recommendation precision and accuracy.

Findings

Modified tf-idf and stemming improve accuracy by 2-3%.

User model updates from usage history increase system precision.

System outperforms Glove and Word2Vec in experimental tests.

Abstract

With the recent increase in data online, discovering meaningful opportunities can be time-consuming and complicated for many individuals. To overcome this data overload challenge, we present a novel text-content-based recommender system as a valuable tool to predict user interests. To that end, we develop a specific procedure to create user models and item feature-vectors, where items are described in free text. The user model is generated by soliciting from a user a few keywords and expanding those keywords into a list of weighted near-synonyms. The item feature-vectors are generated from the textual descriptions of the items, using modified tf-idf values of the users' keywords and their near-synonyms. Once the users are modeled and the items are abstracted into feature vectors, the system returns the maximum-similarity items as recommendations to that user. Our experimental evaluation shows that our method of creating the user models and item feature-vectors resulted in higher precision and accuracy in comparison to well-known feature-vector-generating methods like Glove and Word2Vec. It also shows that stemming and the use of a modified version of tf-idf increase the accuracy and precision by 2% and 3%, respectively, compared to non-stemming and the standard tf-idf definition. Moreover, the evaluation results show that updating the user model from usage histories improves the precision and accuracy of the system. This recommender system has been developed as part of the Agnes application, which runs on iOS and Android platforms and is accessible through the Agnes website.