The goal of the Facebook recommendation engine is to compare and rank heterogeneous types of content in order to find the most relevant recommendations based on user preference and page context. The challenges for such a recommendation engine include several aspects: 1) the online queries being processed are at very large scale; 2) with new content types and new user-generated content constantly added to the system, the candidate object set and underlying data distribution change rapidly; 3) different types of content usually have very distinct characteristics, which makes generic feature engineering difficult; and 4) unlike a search engine that can capture intention of users based on their search queries, our recommendation engine needs to focus more on users' profile and interests, past behaviors and current actions in order to infer their cognitive states. In this presentation, we would like to introduce an effective, scalable, online machine learning framework we developed in order to address the aforementioned challenges. We also want to discuss the insights, approaches and experiences we have accumulated during our research and development process.

MOTIVATION: Time series expression experiments have emerged as a popular method for studying a wide range of biological systems under a variety of conditions. One advantage of such data is the ability to infer regulatory relationships using time lag analysis. However, such analysis in a single experiment may result in many false positives due to the small number of time points and the large number of genes. Extending these methods to simultaneously analyze several time series datasets is challenging since under different experimental conditions biological systems may behave faster or slower making it hard to rely on the actual duration of the experiment. RESULTS: We present a new computational model and an associated algorithm to address the problem of inferring time-lagged regulatory relationships from multiple time series expression experiments with varying (unknown) time-scales. Our proposed algorithm uses a set of known interacting pairs to compute a temporal transformation between every two datasets. Using this temporal transformation we search for new interacting pairs. As we show, our method achieves a much lower false-positive rate compared to previous methods that use time series expression data for pairwise regulatory relationship discovery. Some of the new predictions made by our method can be verified using other high throughput data sources and functional annotation databases. AVAILABILITY: Matlab implementation is available from the supporting website: http://www.cs.cmu.edu/~yanxins/regulation_inference/index.html.

The increasing interest in supporting multiparty remote collaboration has created both opportunities and challenges for the research community. The research reported here aims to develop tools to support multiparty remote collaborations and to study human behaviors using these tools. In this paper we first introduce an experimental multimedia (video and audio) system with which an expert can collaborate with several novices. We then use this system to study helpers' focus of attention (FOA) during a collaborative circuit assembly task. We investigate the relationship between FOA and language as well as activities using multimodal (audio and video) data, and use learning methods to predict helpers' FOA. We process different modalities separately and fusion the results to make a final decision. We employ a sliding window-based delayed labeling method to automatically predict changes in FOA in real time using only the dialogue among the helper and workers. We apply an adaptive background subtraction method and support vector machine to recognize the worker's activities from the video. To predict the helper's FOA, we make decisions using the information of joint project boundaries and workers' recent activities. The overall prediction accuracies are 79.52% using audio only and 81.79% using audio and video combined.