In this paper, we aim at tackling the problem of dynamic user profiling in the context of streams of short texts. Profiling users' expertise in such context is more challenging than in the case of long documents in static collection as it is difficult to track users' dynamic expertise in streaming sparse data. To obtain better profiling performance, we propose a streaming profiling algorithm (SPA). SPA first utilizes the proposed user expertise tracking topic model (UET) to track the changes of users' dynamic expertise and then utilizes the proposed streaming keyword diversification algorithm (SKDA) to produce top-k diversified keywords for profiling users' dynamic expertise at a specific point in time. Experimental results validate the effectiveness of the proposed algorithms.
Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify review quality in terms of its helpfulness to the end consumers. In contrast to previous works in this domain exploiting a variety of syntactic and community-level features, we delve deep into the semantics of reviews as to what makes them useful, providing interpretable explanation for the same. We identify a set of consistency and semantic factors, all from the text, ratings, and timestamps of user-generated reviews, making our approach generalizable across all communities and domains. We explore review semantics in terms of several latent factors like the expertise of its author, his judgment about the fine-grained facets of the underlying product, and his writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii) item facets, and (iii) review helpfulness. Large-scale experiments on five real-world datasets from Amazon show significant improvement over state-of-the-art baselines in predicting and ranking useful reviews.
SHFAYIM, Israel (Reuters) - Technology that has helped Israel's military drive tanks, guide and intercept missiles, and keep its computer systems secure is being redeployed in the development of driverless cars. Investment from firms seeking access to Israeli expertise in automated driving, much of it gathered by engineers during their conscription, is pouring into startups. U.S. chipmaker Intel, German auto supplier Continental, Samsung, Daimler, Ford Motor Co and GM are among those to have bought startups or set up their own development centers in Israel. Inexperience in car-making, distance from traditional auto centers and competition from other tech sectors for top staff are a challenge for investors. Israeli auto tech startups still raised almost as much as similar U.S. companies last year.
StoreDot, which raised $60 million from Daimler in September and another $20 million from BP on Tuesday, developed a super-fast charger for cellphones before switching to automotives. The company, valued at $750 million according to startup marketplace Funderbeam, says its batteries can fully charge an electric vehicle in five minutes.