PKDD 2010 - PDF Preprint Papers

PKDD 2010 European Symposium/Conference on Principles of Data Mining and Knowledge Discovery

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PKDD
2010
Springer

313views Data Mining» more PKDD 2010»

Topic Modeling for Personalized Recommendation of Volatile Items

13 years 9 months ago

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One of the major strengths of probabilistic topic modeling is the ability to reveal hidden relations via the analysis of co-occurrence patterns on dyadic observations, such as docu...

Maks Ovsjanikov, Ye Chen

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Id	Views	Title	Status

1	313	Topic Modeling for Personalized Recommendation of Volatile Items	claim
2	235	Online Structural Graph Clustering Using Frequent Subgraph Mining	claim
3	212	Cross Validation Framework to Choose amongst Models and Datasets for Transfer Learning	claim
4	194	Weighted Symbols-Based Edit Distance for String-Structured Image Classification	claim
5	193	Process Mining Meets Abstract Interpretation	claim
6	188	AnswerArt - Contextualized Question Answering	claim
7	188	Semi-supervised Abstraction-Augmented String Kernel for Multi-level Bio-Relation Extraction	claim
8	184	Shift-Invariant Grouped Multi-task Learning for Gaussian Processes	claim
9	183	Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes	claim
10	183	Classification and Novel Class Detection of Data Streams in a Dynamic Feature Space	claim
11	179	Laplacian Spectrum Learning	claim
12	179	Learning an Affine Transformation for Non-linear Dimensionality Reduction	claim
13	179	Gaussian Processes for Sample Efficient Reinforcement Learning with RMAX-Like Exploration	claim
14	178	Large-Scale Support Vector Learning with Structural Kernels	claim
15	178	Graph Regularized Transductive Classification on Heterogeneous Information Networks	claim
16	177	ITCH: Information-Theoretic Cluster Hierarchies	claim
17	172	Surprising Patterns for the Call Duration Distribution of Mobile Phone Users	claim
18	169	Efficient and Numerically Stable Sparse Learning	claim
19	169	Classification with Sums of Separable Functions	claim
20	168	Bayesian Knowledge Corroboration with Logical Rules and User Feedback	claim
21	166	A Cluster-Level Semi-supervision Model for Interactive Clustering	claim
22	164	Efficient Planning in Large POMDPs through Policy Graph Based Factorized Approximations	claim
23	164	Complexity Bounds for Batch Active Learning in Classification	claim
24	162	Expectation Propagation for Bayesian Multi-task Feature Selection	claim
25	160	Entropy and Margin Maximization for Structured Output Learning	claim
26	160	Sparse Unsupervised Dimensionality Reduction Algorithms	claim
27	158	Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach	claim
28	155	Latent Structure Pattern Mining	claim
29	154	Topic Models Conditioned on Relations	claim
30	153	A Robustness Measure of Association Rules	claim
31	152	CET: A Tool for Creative Exploration of Graphs	claim
32	152	Demand-Driven Tag Recommendation	claim
33	152	Online Knowledge-Based Support Vector Machines	claim
34	150	Virus Propagation on Time-Varying Networks: Theory and Immunization Algorithms	claim
35	148	Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models	claim
36	148	Online Learning in Adversarial Lipschitz Environments	claim
37	146	Nonparametric Bayesian Clustering Ensembles	claim
38	143	Competitive Online Generalized Linear Regression under Square Loss	claim
39	143	A Unified Approach to Active Dual Supervision for Labeling Features and Examples	claim
40	141	On Detecting Clustered Anomalies Using SCiForest	claim
41	138	Constructing Nonlinear Discriminants from Multiple Data Views	claim
42	131	Flu Detector - Tracking Epidemics on Twitter	claim
43	129	Smarter Sampling in Model-Based Bayesian Reinforcement Learning	claim
44	129	Learning Algorithms for Link Prediction Based on Chance Constraints	claim
45	129	Mining Billion-Node Graphs: Patterns, Generators and Tools	claim
46	128	Learning to Tag from Open Vocabulary Labels	claim
47	127	Software-Defect Localisation by Mining Dataflow-Enabled Call Graphs	claim
48	125	NDPMine: Efficiently Mining Discriminative Numerical Features for Pattern-Based Classification	claim
49	124	Summarising Data by Clustering Items	claim
50	122	Detecting Events in a Million New York Times Articles	claim
51	122	Exploration in Relational Worlds	claim
52	113	Predictive Distribution Matching SVM for Multi-domain Learning	claim

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