User Profile Based Research Paper Recommendation
Harshita Sahijwani, Sourish Dasgupta
TL;DR
This paper presents a research paper recommender system that adapts to user preferences over time by analyzing feedback and evolving thematic interests using topic modeling.
Contribution
It introduces a novel feedback-driven recommendation approach that captures changing user interests through topic-modeling of user interactions.
Findings
Improved relevance of recommended papers based on user feedback
Effective modeling of evolving user interests over time
Enhanced personalization in research paper recommendations
Abstract
We design a recommender system for research papers based on topic-modeling. The users feedback to the results is used to make the results more relevant the next time they fire a query. The user's needs are understood by observing the change in the themes that the user shows a preference for over time.
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Taxonomy
TopicsTopic Modeling · Recommender Systems and Techniques · Expert finding and Q&A systems