A Semantic VSM-Based Recommender System

TL;DR

This paper investigates how incorporating ontologies and data mining techniques into content-based recommender systems enhances their ability to suggest relevant forum topics, thereby reducing information overload and increasing user engagement.

Contribution

It introduces a novel method that enriches vector space model-based recommender systems with ontologies derived from text mining and NLP, improving recommendation performance.

Findings

Proposed RS outperforms simple VSM-based RS in accuracy.

Ontology-enriched RS increases relevance of topic suggestions.

Use of domain ontologies improves user satisfaction.

Abstract

Online forums enable users to discuss together around various topics. One of the serious problems of these environments is high volume of discussions and thus information overload problem. Unfortunately without considering the users interests, traditional Information Retrieval (IR) techniques are not able to solve the problem. Therefore, employment of a Recommender System (RS) that could suggest favorite's topics of users according to their tastes could increases the dynamism of forum and prevent the users from duplicate posts. In addition, consideration of semantics can be useful for increasing the performance of IR based RS. Our goal is study of impact of ontology and data mining techniques on improving of content-based RS. For this purpose, at first, three type of ontologies will be constructed from the domain corpus with utilization of text mining, Natural Language Processing (NLP) and Wordnet and then they will be used as an input in two kind of RS: one, fully ontology-based and one with enriching the user profile vector with ontology in vector space model (VSM) (proposed method). Afterward the results will be compared with the simple VSM based RS. Given results show that the proposed RS presents the highest performance.