15 research outputs found
Alpenglow: Open source recommender framework with time-Aware learning and evaluation
International audienc
Location-aware online learning for top-k recommendation
We address the problem of recommending highly volatile items for users, both with potentially ambiguous location that may change in time. The three main ingredients of our method include (1) using online machine learning for the highly volatile items; (2) learning the personalized importance of hierarchical geolocation (for example, town, region, country, continent); finally (3) modeling temporal relevance by counting recent items with an exponential decay in recency.For (1), we consider a time-aware setting, where evaluation is cumbersome by traditional measures since we have different top recommendations at different times. We describe a time-aware framework based on individual item discounted gain. For (2), we observe that trends and geolocation turns out to be more important than personalized user preferences: user-item and content-item matrix factorization improves in combination with our geo-trend learning methods, but in itself, they are greatly inferior to our location based models. In fact, since our best performing methods are based on spatiotemporal data, they are applicable in the user cold start setting as well and perform even better than content based cold start methods. Finally for (3), we estimate the probability that the item will be viewed by its previous views to obtain a powerful model that combines item popularity and recency.To generate realistic data for measuring our new methods, we rely on Twitter messages with known GPS location and consider hashtags as items that we recommend the users to be included in their next message. © 2016 Elsevier B.V
Raising graphs from randomness to reveal information networks
We analyze the fine-grained connections between the aver- age degree and the power-law degree distribution exponent in growing information networks. Our starting observation is a power-law degree distribution with a decreasing expo- nent and increasing average degree as a function of the net- work size. Our experiments are based on three Twitter at- mention networks and three more from the Koblenz Network Collection. We observe that popular network models cannot explain decreasing power-law degree distribution exponent and increasing average degree at the same time. We propose a model that is the combination of exponential growth, and a power-law developing network, in which new "homophily" edges are continuously added to nodes propor- tional to their current homophily degree. Parameters of the average degree growth and the power-law degree distribu- tion exponent functions depend on the ratio of the network growth exponent parameters. Specifically, we connect the growth of the average degree to the decreasing exponent of the power-law degree distribution. Prior to our work, only one of the two cases were handled. Existing models and even their combinations can only reproduce some of our key new observations in growing information networks. © 2017 ACM
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APOE4/4 is linked to damaging lipid droplets in Alzheimers disease microglia.
Several genetic risk factors for Alzheimers disease implicate genes involved in lipid metabolism and many of these lipid genes are highly expressed in glial cells1. However, the relationship between lipid metabolism in glia and Alzheimers disease pathology remains poorly understood. Through single-nucleus RNA sequencing of brain tissue in Alzheimers disease, we have identified a microglial state defined by the expression of the lipid droplet-associated enzyme ACSL1 with ACSL1-positive microglia being most abundant in patients with Alzheimers disease having the APOE4/4 genotype. In human induced pluripotent stem cell-derived microglia, fibrillar Aβ induces ACSL1 expression, triglyceride synthesis and lipid droplet accumulation in an APOE-dependent manner. Additionally, conditioned media from lipid droplet-containing microglia lead to Tau phosphorylation and neurotoxicity in an APOE-dependent manner. Our findings suggest a link between genetic risk factors for Alzheimers disease with microglial lipid droplet accumulation and neurotoxic microglia-derived factors, potentially providing therapeutic strategies for Alzheimers disease