A novel recommendation system to match college events and groups to students
Kazem Qazanfari, Abdou Youssef, Kai Keane, Joseph Nelson

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…
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