wsdm 2014 论文列表Seventh ACM International Conference on Web Search and Data Mining, WSDM 2014, New York, NY, USA, February 24-28, 2014. |
Data design for personalization: current challenges and emerging opportunities.
Workshop on large-scale and distributed systems for information retrieval (LSDS-IR 2014).
1st workshop on diffusion networks and cascade analytics.
Web-scale classification: web classification in the big data era.
Ioannis Partalas Massih-Reza Amini Ion Androutsopoulos Thierry Artières Patrick Gallinari Éric Gaussier Georgios Paliouras
Log-based personalization: the 4th web search click data (WSCD) workshop.
Entity linking and retrieval for semantic search.
Multilingual probabilistic topic modeling and its applications in web mining and search.
Diversity and novelty in web search, recommender systems and data streams.
Big graph mining for the web and social media: algorithms, anomaly detection, and applications.
Exploration and mining of web repositories.
Behavioral data mining and network analysis in massive online games.
Search by multiple examples.
Exploratory search with semantic transformations using collaborative knowledge bases.
On discovering non-obvious recommendations: using unexpectedness and neighborhood selection methods in collaborative filtering systems.
Strategy in action: analyzing online search behavior bymining search strategies.
Integration of large scale knowledge bases using probabilistic graphical models.
Is a picture really worth a thousand words?: - on the role of images in e-commerce.
Visualizing brand associations from web community photos.
Ranking in heterogeneous social media.
Detecting non-gaussian geographical topics in tagged photo collections.
Who watches (and shares) what on youtube? and when?: using twitter to understand youtube viewership.
Understanding and promoting micro-finance activities in Kiva.org.
Inferring the impacts of social media on crowdfunding.
Modelling growth of urban crowd-sourced information.
Trust, but verify: predicting contribution quality for knowledge base construction and curation.
Knowledge-based graph document modeling.
Using linked data to mine RDF from wikipedia's tables.
WebChild: harvesting and organizing commonsense knowledge from the web.
Scalable topic-specific influence analysis on microblogs.
Spatial compactness meets topical consistency: jointly modeling links and content for community detection.
Nonparametric bayesian upstream supervised multi-modal topic models.
Going beyond Corr-LDA for detecting specific comments on news & blogs.
Supervised N-gram topic model.
Latent dirichlet allocation based diversified retrieval for e-commerce search.
Entity linking at the tail: sparse signals, unknown entities, and phrase models.
Sentiment analysis on evolving social streams: how self-report imbalances can help.
Chinese-English mixed text normalization.
Effective co-betweenness centrality computation.
Fast approximation of betweenness centrality through sampling.
Modeling opinion dynamics in social networks.
Learning social network embeddings for predicting information diffusion.
Prediction in a microblog hybrid network using bonacich potential.
Learning latent representations of nodes for classifying in heterogeneous social networks.
Active learning for networked data based on non-progressive diffusion model.
A few good predictions: selective node labeling in a social network.
FENNEL: streaming graph partitioning for massive scale graphs.
Detecting cohesive and 2-mode communities indirected and undirected networks.
Customized tour recommendations in urban areas.
Transferring heterogeneous links across location-based social networks.
Social collaborative retrieval.
Personalized entity recommendation: a heterogeneous information network approach.
Improving pairwise learning for item recommendation from implicit feedback.
On building entity recommender systems using user click log and freebase knowledge.
Who likes it more?: mining worth-recommending items from long tails by modeling relative preference.
Taxonomy discovery for personalized recommendation.
Scalable K-Means by ranked retrieval.
Lessons from the journey: a query log analysis of within-session learning.
The last click: why users give up information network navigation.
User modeling in search logs via a nonparametric bayesian approach.
Modeling dwell time to predict click-level satisfaction.
Discovering common motifs in cursor movement data for improving web search.
LASER: a scalable response prediction platform for online advertising.
An efficient framework for online advertising effectiveness measurement and comparison.
Scalable hierarchical multitask learning algorithms for conversion optimization in display advertising.
Estimating ad group performance in sponsored search.
Partner tiering in display advertising.
Predicting response in mobile advertising with hierarchical importance-aware factorization machine.
Exploiting contextual factors for click modeling in sponsored search.
Sampling dilemma: towards effective data sampling for click prediction in sponsored search.
Search engine click spam detection based on bipartite graph propagation.
Adapting deep RankNet for personalized search.
Relative confidence sampling for efficient on-line ranker evaluation.
Democracy is good for ranking: towards multi-view rank learning and adaptation in web search.
Struggling or exploring?: disambiguating long search sessions.
Improving search relevance for short queries in community question answering.
Exploiting user disagreement for web search evaluation: an experimental approach.
Heterogeneous graph-based intent learning with queries, web pages and Wikipedia concepts.
A self-adapting latency/power tradeoff model for replicated search engines.
Improving the efficiency of multi-site web search engines.
Data that matter: opportunities in crisis informatics research.