The iterative continuum of scientific production generates a need for filtering and specific crossing of ideas and papers. In this paper, we present BIBLME RecSys software which is dedicated to the analysis of bibliographical references extracted from scientific collections of papers. Our goal is to... || 【随意看】Recommender systems, Text mining, Digital libraries, Bib- liographic information, Bibliometrics.; || Anaı̈s Ollagnier, Sébastien Fournier, and Patrice Bellot...
While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in ad... || 【随意看】content-based; || Toine Bogers...
Venue category recommendation is an essential application for the tourism and advertisement industries, wherein it may sug- gest attractive localities within close proximity to users’ current location. Considering that many adults use more than three so- cial networks simultaneously, it is reasonabl... || Grassmannn manifold; group knowledge; Spectral clustering; 谱聚类；SIGIR; || Aleksandr Farseev*, Ivan Samborskii** *, Andrey Filchenkov**, Tat-Seng Chua*...
A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task i... || Transfer learning, survey, machine learning, data mining.; || Sinno Jialin Pan and Qiang Yang...
In order to recommend products to users, we must ultimately predict how a user will respond to a new product. To do so we must uncover the implicit tastes of each user as well as the properties of each product. For example, in order to predict whether a user will enjoy Harry Potter, it helps to iden... || recommender systems, topic models, librec; || Julian McAuley; Jure Leskovec...
Collaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users.
Fortunately, with the continuous growth of onlin... || Recommender Systems; Sentiment Analysis; Collaborative Filtering; Recommendation Explanation; EFM; || Yongfeng Zhang...
Gene expression profiling assays are frequently used to guide adjuvant chemotherapy decisions in hormone receptor–positive, lymph node–negative breast cancer. We hypothesized that the clinical value of these new tools would be more fully realized when appropriately integrated with high-quality clini... || 21gene 预测；预测模型中的假设检验；乳腺癌症；医学机器学习; || Hyun-seok Kim...
