Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System

Abstract

Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities.

Real-Time Ideation Analyzer and Information Recommender

“…BERT Sentence generated more relevant recommendations than BERTopic but yielded one useless recommendation. LLaMA 2 struggled with sentence-based ideation, only being slightly better than the LDA baseline from our previous work [39].…”

“…The adaption process requires time and thought as it is necessary to provide meaningful recommendations that do not harm users' cognition. As described in our previous works [39,40,59], the user should not have cognition overload due to bad interaction design while being cognitively within the research process on our platform. The usability study shows deficits in our current interaction design that need to be addressed, such as a suboptimal abstract presentation, a lack of loading icons and underlying speed issues, a lack of navigation bars showing users where they are currently in the ideation process, and missing buttons and interaction functionality.…”

“…The topic of the vectorized text is then identified with a topic modeling approach before the textual similarity is calculated. In our previous works, we examined the combination of the topic models LDA, LSA, and NMF, along with the similarity algorithms Manhatten, cosine, Jensen, Jaccard, Euclidean, Chebyshev, and Canberra [39]. Alternatively, the pretrained transformer-based approach can be used.…”

“…Our previously proposed approach utilizing LDA [39] is considered our baseline. We use the scikit-learn LDA model with 90 topics, 100 iterations, and a learning rate decay of 0.5.…”

“…As we discussed in our previous work regarding recommendation systems based on topic modeling [39], allowing humans to judge the relevance of recommended items is a good means of validating a model's ability to recommend items. We decided to expand on that human validation approach and let four humans, whom we regard as experts in the field of information science, examine the recommendations that the models provided.…”