At the Sixth International Conference on Learning Representations, Jannis Bulian and Neil Houlsby, researchers at Google AI, presented a paper that shed light on new methods they're testing to improve search results. While publishing a paper certainly doesn't mean the methods are being used, or even will be, it likely increases the odds when the results are highly successful. And when those methods also combine with other actions Google is taking, one can be almost certain. I believe this is happening, and the changes are significant for search engine optimization specialists (SEOs) and content creators. Let's start with the basics and look topically at what's being discussed.
The component parts of a successful search engine optimization (SEO) strategy may have remained relatively constant, but their definition and purpose have changed entirely. Driven by trends like visual search and voice search, the industry's scope has expanded and evolved into something more dynamic. This delivers on a genuine consumer need. According to a report from Slyce.it, 74 percent of shoppers report that text-only search is insufficient for finding the products they want. It is unsurprising that Gartner research predicts that by 2021, early adopter brands that redesign their websites to support visual and voice search will increase digital commerce revenue by as much as 30 percent.
There has been mixed success in applying semantic component analysis (LSA, PLSA, discrete PCA, etc.) to information retrieval. Previous experiments have shown that high-fidelity language models do not imply good quality retrieval. Here we combine link analysis with discrete PCA (a semantic component method) to develop an auxiliary score for information retrieval that is used in post-filtering documents retrieved via regular Tf.Idf methods. For this, we use a topic-specific version of link analysis based on topics developed automatically via discrete PCA methods. To evaluate the resultant topic and link based scoring, a demonstration has been built using the Wikipedia, the public domain encyclopedia on the web.
By 2020, 30% of all website sessions will be conducted without a screen. Now, you may be asking yourself, how is that possible? It turns out that voice-only search allows users to browse the web the Internet and consumer information without actually having to scroll through sites on desktops and mobile devices. And this new technology may be the key to successful brands in the future. Voice search essentially allows users to speak into a device as opposed to typing keywords into a search query to generate results.
Planning in partially observable environments remains a challenging problem, despite significant recent advances in offline approximation techniques. A few online methods have also been proposed recently, and proven to be remarkably scalable, but without the theoretical guarantees of their offline counterparts. Thus it seems natural to try to unify offline and online techniques, preserving the theoretical properties of the former, and exploiting the scalability of the latter. In this paper, we provide theoretical guarantees on an anytime algorithm for POMDPs which aims to reduce the error made by approximate offline value iteration algorithms through the use of an efficient online searching procedure. The algorithm uses search heuristics based on an error analysis of lookahead search, to guide the online search towards reachable beliefs with the most potential to reduce error.