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KDD
2012
ACM
187views Data Mining» more  KDD 2012»
11 years 11 months ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...
WSDM
2012
ACM
301views Data Mining» more  WSDM 2012»
12 years 4 months ago
Learning evolving and emerging topics in social media: a dynamic nmf approach with temporal regularization
As massive repositories of real-time human commentary, social media platforms have arguably evolved far beyond passive facilitation of online social interactions. Rapid analysis o...
Ankan Saha, Vikas Sindhwani
KDD
2009
ACM
245views Data Mining» more  KDD 2009»
14 years 9 months ago
Mining rich session context to improve web search
User browsing information, particularly their non-search related activity, reveals important contextual information on the preferences and the intent of web users. In this paper, ...
Guangyu Zhu, Gilad Mishne
KDD
2012
ACM
212views Data Mining» more  KDD 2012»
11 years 11 months ago
Factoring past exposure in display advertising targeting
Online advertising is increasingly becoming more performance oriented, where the decision to show an advertisement to a user is made based on the user’s propensity to respond to...
Neha Gupta, Abhimanyu Das, Sandeep Pandey, Vijay K...
RECSYS
2009
ACM
14 years 3 months ago
FeedbackTrust: using feedback effects in trust-based recommendation systems
With the advent of online social networks, the trust-based approach to recommendation has emerged which exploits the trust network among users and makes recommendations based on t...
Samaneh Moghaddam, Mohsen Jamali, Martin Ester, Ja...