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TKDD
2010
121views more  TKDD 2010»
15 years 5 months ago
Factor in the neighbors: Scalable and accurate collaborative filtering
Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are ana...
Yehuda Koren
TKDE
2010
182views more  TKDE 2010»
15 years 5 months ago
MILD: Multiple-Instance Learning via Disambiguation
In multiple-instance learning (MIL), an individual example is called an instance and a bag contains a single or multiple instances. The class labels available in the training set ...
Wu-Jun Li, Dit-Yan Yeung
TKDE
2010
184views more  TKDE 2010»
15 years 5 months ago
The Dynamic Bloom Filters
—A Bloom filter is an effective, space-efficient data structure for concisely representing a set, and supporting approximate membership queries. Traditionally, the Bloom filter a...
Deke Guo, Jie Wu, Honghui Chen, Ye Yuan, Xueshan L...
TKDE
2010
186views more  TKDE 2010»
15 years 5 months ago
A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis
—Social Tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (songs, pictures, web links, products, etc.). Social tag...
Panagiotis Symeonidis, Alexandros Nanopoulos, Yann...
TKDE
2010
153views more  TKDE 2010»
15 years 5 months ago
Automatic Ontology Matching via Upper Ontologies: A Systematic Evaluation
—“Ontology matching” is the process of finding correspondences between entities belonging to different ontologies. This paper describes a set of algorithms that exploit uppe...
Viviana Mascardi, Angela Locoro, Paolo Rosso
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