Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Improving Case-Based Recommendation: A Collaborative Filtering Approach
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Proceedings of the 10th international conference on Intelligent user interfaces
Improved automatic keyword extraction given more linguistic knowledge
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Effective explanations of recommendations: user-centered design
Proceedings of the 2007 ACM conference on Recommender systems
Discovering key concepts in verbose queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Eddi: interactive topic-based browsing of social status streams
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Recommender Systems Handbook
WiGipedia: A Tool for Improving Structured Data in Wikipedia
ICSC '11 Proceedings of the 2011 IEEE Fifth International Conference on Semantic Computing
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Modeling topic specific credibility on twitter
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
TasteWeights: a visual interactive hybrid recommender system
Proceedings of the sixth ACM conference on Recommender systems
Inspectability and control in social recommenders
Proceedings of the sixth ACM conference on Recommender systems
Credibility in Context: An Analysis of Feature Distributions in Twitter
SOCIALCOM-PASSAT '12 Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
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
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This paper presents LinkedVis, an interactive visual recommender system that combines social and semantic knowledge to produce career recommendations based on the LinkedIn API. A collaborative (social) approach is employed to identify professionals with similar career paths and produce personalized recommendations of both companies and roles. To unify semantically identical but lexically distinct entities and arrive at better user models, we employ lightweight natural language processing and entity resolution using semantic information from a variety of end-points on the web. Elements from the underlying recommendation algorithm are exposed through an interactive interface that allows users to manipulate different aspects of the algorithm and the data it operates on, allowing users to explore a variety of "what-if" scenarios around their current profile. We evaluate LinkedVis through leave-one-out accuracy and diversity experiments on a data corpus collected from 47 users and their LinkedIn connections, as well as through a supervised study of 27 users exploring their own profile and recommendations interactively. Results show that our approach outperforms a benchmark recommendation algorithm without semantic resolution in terms of accuracy and diversity, and that the ability to tweak recommendations interactively by adjusting profile item and social connection weights further improves predictive accuracy. Questionnaires on the user experience with the explanatory and interactive aspects of the application reveal very high user acceptance and satisfaction.