诺库维普

BIBLME RecSys: Harnessing Bibliometric Measures for a Scholarly Paper Recommender System 2018-05-25 14:39:19

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...

Report on RecSys 2014 Workshop on New Trends in Content-Based Recommender Systems 2018-05-25 14:33:48

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...

特征维度处理 2018-04-28 14:23:10

1）笛卡尔积特征组合 2）特征哈希... || 笛卡尔积特征组合；特征哈希；Feature Hashing；Hash trick...

基于离散选择的推荐系统模型与算法 2018-02-03 16:46:24

清华大学，博士论文；离散选择模型；nested logit; || 戚欣；姜海...

Cross-Domain Recommendation via Clustering on Multi-Layer Graphs 2018-01-27 22:48:38

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 Survey on Transfer Learning 2018-01-27 18:48:23

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...

Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text 2018-01-25 15:21:33

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...

WWW 23

Austin, Texas, USA,

AAAI 23

Washington, DC, USA,

SIGIR2022

Madrid, Spain, July 11th to 15th, 2022

WSDM2023

TBD,

IJCAI2022

Austria, July 23-29, 2022