Referral Web: combining social networks and collaborative filtering
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Modern Information Retrieval
The Journal of Machine Learning Research
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Applying discrete PCA in data analysis
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Expertise modeling for matching papers with reviewers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
New perspectives and methods in link prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Combined regression and ranking
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic level expertise search over heterogeneous networks
Machine Learning
CollabSeer: a search engine for collaboration discovery
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
A large scale machine learning system for recommending heterogeneous content in social networks
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Collaborative topic modeling for recommending scientific articles
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation
Proceedings of the fifth ACM conference on Recommender systems
Rao-blackwellised particle filtering for dynamic Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Social tie mining in company networks
Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics
Patent partner recommendation in enterprise social networks
Proceedings of the sixth ACM international conference on Web search and data mining
LCARS: a location-content-aware recommender system
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to predict reciprocity and triadic closure in social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Inferring anchor links across multiple heterogeneous social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Supporting exploratory people search: a study of factor transparency and user control
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Analysis of a context-aware recommender system model for smart urban environment
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
What users care about: a framework for social content alignment
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Cross domain recommendation based on multi-type media fusion
Neurocomputing
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Interdisciplinary collaborations have generated huge impact to society. However, it is often hard for researchers to establish such cross-domain collaborations. What are the patterns of cross-domain collaborations? How do those collaborations form? Can we predict this type of collaborations? Cross-domain collaborations exhibit very different patterns compared to traditional collaborations in the same domain: 1) sparse connection: cross-domain collaborations are rare; 2) complementary expertise: cross-domain collaborators often have different expertise and interest; 3) topic skewness: cross-domain collaboration topics are focused on a subset of topics. All these patterns violate fundamental assumptions of traditional recommendation systems. In this paper, we analyze the cross-domain collaboration data from research publications and confirm the above patterns. We propose the Cross-domain Topic Learning (CTL) model to address these challenges. For handling sparse connections, CTL consolidates the existing cross-domain collaborations through topic layers instead of at author layers, which alleviates the sparseness issue. For handling complementary expertise, CTL models topic distributions from source and target domains separately, as well as the correlation across domains. For handling topic skewness, CTL only models relevant topics to the cross-domain collaboration. We compare CTL with several baseline approaches on large publication datasets from different domains. CTL outperforms baselines significantly on multiple recommendation metrics. Beyond accurate recommendation performance, CTL is also insensitive to parameter tuning as confirmed in the sensitivity analysis.