Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
A Unified View of Matrix Factorization Models
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Characterizing user behavior in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Tensor Decompositions and Applications
SIAM Review
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
Is it really about me?: message content in social awareness streams
Proceedings of the 2010 ACM conference on Computer supported cooperative work
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Gradient descent optimization of smoothed information retrieval metrics
Information Retrieval
An empirical study on learning to rank of tweets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Predicting popular messages in Twitter
Proceedings of the 20th international conference companion on World wide web
Characterizing search intent diversity into click models
Proceedings of the 20th international conference on World wide web
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
Speak little and well: recommending conversations in online social streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Identifying relevant social media content: leveraging information diversity and user cognition
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Collaborative competitive filtering: learning recommender using context of user choice
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
User-click modeling for understanding and predicting search-behavior
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
OrdRec: an ordinal model for predicting personalized item rating distributions
Proceedings of the fifth ACM conference on Recommender systems
Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy
Proceedings of the fifth ACM conference on Recommender systems
Factorization Machines with libFM
ACM Transactions on Intelligent Systems and Technology (TIST)
Bimodal invitation-navigation fair bets model for authority identification in a social network
Proceedings of the 21st international conference on World Wide Web
Co-factorization machines: modeling user interests and predicting individual decisions in Twitter
Proceedings of the sixth ACM international conference on Web search and data mining
Ranking in information streams
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Large-scale social recommender systems: challenges and opportunities
Proceedings of the 22nd international conference on World Wide Web companion
GAPfm: optimal top-n recommendations for graded relevance domains
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Diffusion-aware personalized social update recommendation
Proceedings of the 7th ACM conference on Recommender systems
Improving pairwise learning for item recommendation from implicit feedback
Proceedings of the 7th ACM international conference on Web search and data mining
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As online social media further integrates deeper into our lives, we spend more time consuming social update streams that come from our online connections. Although social update streams provide a tremendous opportunity for us to access information on-the-fly, we often complain about its relevance. Some of us are flooded with a steady stream of information and simply cannot process it in full. Ranking the incoming content becomes the only solution for the overwhelmed users. For some others, in contrast, the incoming information stream is pretty weak, and they have to actively search for relevant information which is quite tedious. For these users, augmenting their incoming content flow with relevant information from outside their first-degree network would be a viable solution. In that case, the problem of relevance becomes even more prominent. In this paper, we start an open discussion on how to build effective systems for ranking social updates from a unique perspective of LinkedIn -- the largest professional network in the world. More specifically, we address this problem as an intersection of learning to rank, collaborative filtering, and clickthrough modeling, while leveraging ideas from information retrieval and recommender systems. We propose a novel probabilistic latent factor model with regressions on explicit features and compare it with a number of non-trivial baselines. In addition to demonstrating superior performance of our model, we shed some light on the nature of social updates on LinkedIn and how users interact with them, which might be applicable to social update streams in general.