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

09/24/2017
by   Kazem Qazanfari, et al.
0

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 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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

06/15/2020

User Profiling from Reviews for Accurate Time-Based Recommendations

Recommender systems are a valuable way to engage users in a system, incr...
09/12/2021

An Improved Hybrid Recommender System: Integrating Document Context-Based and Behavior-Based Methods

One of the main challenges in recommender systems is data sparsity which...
03/23/2021

Diversity Regularized Interests Modeling for Recommender Systems

With the rapid development of E-commerce and the increase in the quantit...
08/30/2020

Beyond Next Item Recommendation: Recommending and Evaluating List of Sequences

Recommender systems (RS) suggest items-based on the estimated preference...
11/01/2021

URIR: Recommendation algorithm of user RNN encoder and item encoder based on knowledge graph

Due to a large amount of information, it is difficult for users to find ...
12/30/2019

Consistency-Aware Recommendation for User-Generated ItemList Continuation

User-generated item lists are popular on many platforms. Examples includ...
04/12/2017

Determining Song Similarity via Machine Learning Techniques and Tagging Information

The task of determining item similarity is a crucial one in a recommende...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.