With the rapid growth of scientific publications, researchers need to spend more time and effort searching for papers that align with their research interests. To address this challenge, paper recommendation systems have been developed to help researchers in effectively identifying relevant paper. One of the leading approaches to paper recommendation is content-based filtering method. Traditional content-based filtering methods recommend relevant papers to users based on the overall similarity of papers. However, these approaches do not take into account the information seeking behaviors that users commonly employ when searching for literature. Such behaviors include not only evaluating the overall similarity among papers, but also focusing on specific sections, such as the method section, to ensure that the approach aligns with the user’s interests. In this paper, we propose a content-based filtering recommendation method that takes this information seeking behavior into account. Specifically, in addition to considering the overall content of a paper, our approach also takes into account three specific sections (background, method, and results) and assigns weights to them to better reflect user preferences. We conduct offline evaluations on the publicly available DBLP dataset, and the results demonstrate that the proposed method outperforms six baseline methods in terms of precision, recall, F1-score, MRR, and MAP.

Twitter is noisy and contains many nonstandard words. Furthermore, in Japanese tweets, many words have multiple variant notations. Therefore, the use of such noisy data may interfere with tasks such as identifying potential communities. In this paper, based on the assumption that words with the same meaning will have similar related images, we propose a method of normalization for nonstandard words and variant notations in Japanese tweets using related images. First, we collect images related to a word from Bing and use OpponentSIFT features to properly represent the content of those images. Next, we use clustering to narrow down the set of images to extract related images. Finally, we determine the similarity between words based on the similarity of the sets of related images.