An LDA-Based Approach to Scientific Paper Recommendation

Abstract

Recommendation of scientific papers is a task aimed to support researchers in accessing relevant articles from a large pool of unseen articles. When writing a paper, a researcher focuses on the topics related to her/his scientific domain, by using a technical language. The core idea of this paper is to exploit the topics related to the researchers scientific production (authored articles) to formally define her/his profile; in particular we propose to employ topic modeling to formally represent the user profile, and language modeling to formally represent each unseen paper. The recommendation technique we propose relies on the assessment of the closeness of the language used in the researchers papers and the one employed in the unseen papers. The proposed approach exploits a reliable knowledge source for building the user profile, and it alleviates the cold-start problem, typical of collaborative filtering techniques. We also present a preliminary evaluation of our approach on the DBLP.

A novel hybrid publication recommendation system using compound information

“…When we do not have these information, it cannot work well. Besides, a LDA-based method was proposed to recommendation papers [1] which employed topic models to build the users' profile based on their published papers and language model to get the topics' distribution of papers, leveraged their similarity to present the ranking scores to recommend papers. Some other approaches were also proposed which used latent topic models to recommend papers by modeling citation links jointly [28,34], such as Link-PLSA-LDA and TopicSim.…”

“…The mainstream methods find the most relevant papers to the input keywords according to their relevance on contents (including title, keyword, abstract and sometimes the full paper). The relevance was firstly measured with traditional Information Retrieval techniques, and then improved with topic models [1,34]. However, since there are always a large number of papers sharing the same hot topic, the top-K recommendation results based on paper contents only usually do not have a high precision.…”

A Review on Personalized Academic Paper Recommendation

“…K. Sugiyama et alconducted academic paper recommendations based on the user's recent research interests (Sugiyama & Kan, 2010). Amami et al proposed an academic paper recommendation method based on the LDA topic model (Amami et al, 2016), and carried out pretest on DBLP, the recommended effect is ideal.…”

Latent Factor-based Rating Feedback Learning for Restaurants Recommendation

“…Chao Li, Srn Feng et al applied a LDA and WordNet combined algorithm to mining dynamics of research topics and got kind of great results[3]. Maha Amami et al applied LDA-Based Approach to scientific paper recommendation [4]. Shinjee Pyo et al used LDA models to analysis the TV user groups and TV program, group similar TV users and associate description words for watched TV programs at the same time in a unified topic modeling framework [5].…”