Video recommendation is an important approach for helping people to access interesting videos. In this paper, we propose a scheme to integrate rich information for video recommendation. We regard video recommendation as a ranking problem and generate multiple ranking lists by exploring different information sources. A multi-task rank aggregation approach is proposed to integrate the ranking lists for different users in a joint manner. Our scheme is flexible and can easily incorporate other methods by adding their generated ranking lists into our multi-task learning algorithm. We conduct experiments with 76 users and more than 10,000 videos. The results demonstrate the feasibility and effectiveness of our approach.

In numerous economic scenarios, contracting parties may not have a clear picture of all the relevant aspects. While confronted with these unawareness issues, the strategic decisions of the contracting parties critically depend on their sophistication. A contracting party may be unaware of what she is entitled to determine. Therefore, she can only infer some missing pieces via the contract offered by other parties and determine whether to accept the contract based on her own evaluation of how reasonable the contract is. Further, a contracting party may actively gather information and collect evidence about all possible contingencies to avoid to be trapped into the contractual agreement. In this paper, we propose a general framework to investigate these strategic interactions with unawareness, reasoning, and cognition. We build our conceptual framework upon the classical principal-agent relationship and compare the equilibrium behaviors under various degrees of the unaware agent's sophistication. Several implications regarding optimal contract design, possible exploitation, and cognitive thinking are also presented.

A time series (or equivalently a data stream) consists of data arriving in time order. Single or multiple data streams arise in fields including physics, finance, medicine, and music, to name a few. Often the data comes from sensors (in physics and medicine for example) whose data rates continue to improve dramatically as sensor technology improves and as the number of sensors increases. So fast algorithms become ever more critical in order to distill knowledge from the data. This paper presents our recent work regarding the incremental computation of various primitives: windowed correlation, matching pursuit, sparse null space discovery and elastic burst detection. The incremental idea reflects the fact that recent data is more important than older data. Our StatStream system contains an implementation of these algorithms, permitting us to do empirical studies on both simulated and real data.

Data arriving in time order (a data stream) arises in fields including physics, finance, medicine, and music, to name a few. Often the data comes from sensors (in physics and medicine for example) whose data rates continue to improve dramatically as sensor technology improves. Further, the number of sensors is increasing, so correlating data between sensors becomes ever more critical in order to distill knowlege from the data. In many applications such as finance, recent correlations are of far more interest than long-term correlation, so correlation over sliding windows (windowed correlation) is the desired operation. Fast response is desirable in many applications (e.g., to aim a telescope at an activity of interest or to perform a stock trade). These three factors -- data size, windowed correlation, and fast response -- motivate this work.Previous work [10, 14] showed how to compute Pearson correlation using Fast Fourier Transforms and Wavelet transforms, but such techniques don't work for time series in which the energy is spread over many frequency components, thus resembling white noise. For such "uncooperative" time series, this paper shows how to combine several simple techniques -- sketches (random projections), convolution, structured random vectors, grid structures, and combinatorial design -- to achieve high performance windowed Pearson correlation over a variety of data sets.

Financial time series streams are watched closely by millions of traders. What exactly do they look for and how can we help them do it faster? Physicists study the time series emerging from their sensors. The same question holds for them. Musicians produce time series. Consumers may want to compare them. This tutorial presents techniques and case studies for four problems:1. Finding sliding window correlations in financial, physical, and other applications.2. Discovering bursts in large sensor data of gamma rays.3. Matching hums to recorded music, even when people don't hum well.4. Maintaining and manipulating time-ordered data in a database setting.This tutorial draws mostly from the book High Performance Discovery in Time Series: techniques and case studies, Springer-Verlag 2004. You can find the power point slides for this tutorial at http://cs.nyu.edu/cs/faculty/shasha/papers/sigmod04.ppt.The tutorial is aimed at researchers in streams, data mining, and scientific computing. Its applications should interest anyone who works with scientists or financial "quants." The emphasis will be on recent results and open problems. This is a ripe area for further advance.

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