[Paper Review] Natural Language Processing via LDA Topic Model in Recommendation Systems

Hamed Jelodar, Yongli Wang+2 more|arXiv (Cornell University)|Sep 20, 2019

Recommender Systems and Techniques76 references4 citations

TL;DR

This paper proposes a taxonomy of recommendation systems leveraging LDA topic modeling for natural language processing, applying Gibbs sampling to analyze ISWC and WWW conference publications (2013–2017). It demonstrates that LDA-based systems effectively address cold-start, sparsity, and latent user interest modeling, offering a robust alternative to traditional collaborative filtering in online communities.

ABSTRACT

Today, Internet is one of the widest available media worldwide. Recommendation systems are increasingly being used in various applications such as movie recommendation, mobile recommendation, article recommendation and etc. Collaborative Filtering (CF) and Content-Based (CB) are Well-known techniques for building recommendation systems. Topic modeling based on LDA, is a powerful technique for semantic mining and perform topic extraction. In the past few years, many articles have been published based on LDA technique for building recommendation systems. In this paper, we present taxonomy of recommendation systems and applications based on LDA. In addition, we utilize LDA and Gibbs sampling algorithms to evaluate ISWC and WWW conference publications in computer science. Our study suggest that the recommendation systems based on LDA could be effective in building smart recommendation system in online communities.

Motivation & Objective

• To develop a comprehensive taxonomy of recommendation systems based on LDA topic modeling across diverse domains such as app, music, travel, and scientific paper recommendations.
• To evaluate the effectiveness of LDA and Gibbs sampling in analyzing academic publication data from ISWC and WWW conferences (2013–2017).
• To investigate how LDA-based methods mitigate core limitations of collaborative filtering, including cold-start and data sparsity.
• To model and extract latent user interests from textual content without relying on explicit user ratings or prior knowledge.

Proposed method

• Employed Latent Dirichlet Allocation (LDA) as a generative probabilistic model to discover hidden topics in textual data from academic publications.
• Applied Gibbs sampling for approximate inference in LDA to estimate topic distributions over documents and word distributions over topics.
• Constructed a corpus from ISWC and WWW conference papers (2013–2017) using DBLP dataset, treating each paper as a document with words as tokens.
• Used Dirichlet priors (α and η) to model document-topic and topic-word distributions, enabling probabilistic topic inference.
• Mapped latent topics to user and item characteristics to simulate user preferences and item attributes based on semantic content.
• Evaluated the model’s performance in identifying research trends and user interests through topic coherence and semantic analysis.

Experimental results

Research questions

• RQ1 How can LDA-based topic modeling be systematically applied to build recommendation systems across diverse domains such as music, apps, and scientific publications?
• RQ2 To what extent can LDA mitigate the cold-start problem in recommendation systems when new users or items lack sufficient interaction data?
• RQ3 Can LDA effectively reduce data sparsity in collaborative filtering by leveraging semantic content from textual metadata?
• RQ4 How accurately can LDA model latent user interests using only textual content and rating patterns without explicit preference signals?
• RQ5 What insights can be drawn from applying LDA to academic publication data in terms of research trends and researcher interests?

Key findings

• LDA-based recommendation systems effectively address the cold-start problem by modeling new users or items through semantic content rather than relying on interaction history.
• The application of LDA to ISWC and WWW conference publications revealed coherent topic clusters related to research trends and author interests over time.
• Gibbs sampling enabled effective inference in LDA, allowing the extraction of meaningful topics such as 'semantic web', 'knowledge graphs', and 'information retrieval' from academic texts.
• LDA demonstrated utility in dimensionality reduction and exploratory data analysis, improving interpretability and reducing sparsity in content-based recommendation pipelines.
• Topic modeling via LDA successfully captured latent user interests by identifying recurring semantic themes in user-generated content and item descriptions.
• The integration of LDA with collaborative filtering methods enhanced recommendation accuracy and diversity, particularly in sparse rating environments.