Object Recognition with Informative Features and Linear Classification
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
Exploring folksonomy for personalized search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Real-Time Computerized Annotation of Pictures
IEEE Transactions on Pattern Analysis and Machine Intelligence
SheepDog: group and tag recommendation for flickr photos by automatic search-based learning
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Co-reranking by mutual reinforcement for image search
Proceedings of the ACM International Conference on Image and Video Retrieval
Flickr group recommendation based on tensor decomposition
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Suggesting friends using the implicit social graph
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized search on Flickr based on searcher's preference prediction
Proceedings of the 20th international conference companion on World wide web
Context-based friend suggestion in online photo-sharing community
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Modeling social strength in social media community via kernel-based learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Beyond Social Graphs: User Interactions in Online Social Networks and their Implications
ACM Transactions on the Web (TWEB)
Exploiting entities in social media
Proceedings of the sixth international workshop on Exploiting semantic annotations in information retrieval
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The search for entities is the most common search behavior on the Web, especially in social media communities where entities (such as images, videos, people, locations, and tags) are highly heterogeneous and correlated. While previous research usually deals with these social media entities separately, we are investigating in this paper a unified, multi-level, and correlative entity graph to represent the unstructured social media data, through which various applications (e.g., friend suggestion, personalized image search, image tagging, etc.) can be realized more effectively in one single framework. We regard the social media objects equally as "entities" and all of these applications as "entity search" problem which searches for entities with different types. We first construct a multi-level graph which organizes the heterogeneous entities into multiple levels, with one type of entities as vertices in each level. The edges between graphs pairwisely connect the entities weighted by intra-relations in the same level and inter-links across two different levels distilled from the social behaviors (e.g., tagging, commenting, and joining communities). To infer the strength of intra-relations, we propose a circular propagation scheme, which reinforces the mutual exchange of information across different entity types in a cyclic manner. Based on the constructed unified graph, we explicitly formulate entity search as a global optimization problem in a unified Bayesian framework, in which various applications are efficiently realized. Empirically, we validate the effectiveness of our unified entity graph for various social media applications on million-scale real-world dataset.