Dynamic Matrix Factorization with Priors on Unknown Values

Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (\emph{not missing at random}). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an explicit prior on unknown values. When new ratings, users, or items enter the system, we can update the factorization in time independent of the size of data (number of users, items and ratings). Hence, we can quickly recommend items even to very recent users. We test our methods on three large datasets, including two very sparse ones, in static and dynamic conditions. In each case, we outrank state-of-the-art matrix factorization methods that do not use a prior on unknown ratings.Comment: in the Proceedings of 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining 201

WHAT IS THE VALUE OF BT CORN?

A common perception is that the value of Bt corn arises from two components-Bt corn increases expected profit and reduces profit variability. This perception encourages farmers and the policy makers to add a risk benefit to estimates of the value of Bt corn to account for the variability reduction. However, a conceptual model generates a useful decomposition of the value of Bt corn and a condition determining the impact of Bt corn on profit variability. An empirical model finds that Bt corn increases profit variability and thus decreases the value of Bt corn by 10-25% depending on risk preferences.Crop Production/Industries,

Fast Matrix Factorization for Online Recommendation with Implicit Feedback

This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our eALS method consistently outperforms state-of-the-art implicit MF methods. Our implementation is available at https://github.com/hexiangnan/sigir16-eals.Comment: 10 pages, 8 figure

Bean leaf beetles: a current and historical perspective

In 2002, bean leaf beetle populations in Iowa reached their highest levels in 14 years (figure, left). In Iowa, this increase in beetle populations has been partly fueled by weather conditions that favor winter survival, such as mild temperatures (2001-2002: second mildest winter on record) or snow cover (2000-2001: snow cover for 99 consecutive days in central Iowa). The increase in beetle populations has followed the trend for warmer weather during the previous six winters (figure, right)

Bean leaf beetles and soybean planting date

Considering the enormous bean leaf beetle populations in recent years, many soybean growers are interested in options for managing this pest. Cultural control, such as planting date, could be very useful for managing bean leaf beetle. Studies conducted by Larry Pedigo and Mike Zeiss at Iowa State University (1998-1992) quantified the effects of soybean planting date on bean leaf beetle abundance, soybean pod injury, and soybean yield

Corn rootworm insecticides evaluated

Two integrated pest management strategies are used widely to protect corn roots from corn rootworm injury: crop rotation and insecticides. If corn is not rotated, or if extended diapause has been documented to occur in a particular field, then a soil insecticide might be necessary to protect the roots in 2000. The reason we say it might be necessary is because many fields do not have a rootworm population of a sufficient size to cause economic damage. Believe it or not, there are thousands of continuous cornfields across the state in which a rootworm insecticide is not necessary

Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics

Informed sampling-based planning algorithms exploit problem knowledge for better search performance. This knowledge is often expressed as heuristic estimates of solution cost and used to order the search. The practical improvement of this informed search depends on the accuracy of the heuristic. Selecting an appropriate heuristic is difficult. Heuristics applicable to an entire problem domain are often simple to define and inexpensive to evaluate but may not be beneficial for a specific problem instance. Heuristics specific to a problem instance are often difficult to define or expensive to evaluate but can make the search itself trivial. This paper presents Adaptively Informed Trees (AIT*), an almost-surely asymptotically optimal sampling-based planner based on BIT*. AIT* adapts its search to each problem instance by using an asymmetric bidirectional search to simultaneously estimate and exploit a problem-specific heuristic. This allows it to quickly find initial solutions and converge towards the optimum. AIT* solves the tested problems as fast as RRT-Connect while also converging towards the optimum.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2020, 6 + 2 pages, 5 figures, video available at https://youtu.be/twM723QM9T

Biological control of bean leaf beetles

Bean leaf beetles have few known natural enemies and even less is known about the use of these organisms to combat the beetle. Flies, mites, fungi, and nematodes attack bean leaf beetles. Below is a research summary of our current knowledge on the biological control of bean leaf beetle