The goal is to introduce various techniques required to build an IR System. In this course we will explore various methods to solve big data problem. We will evaluate alternative solutions and trade offs. In the later part of the course we will discuss various data mining algorithms to make sense of massive data sets.

Information retrieval and binary classification can be considered equivalent problems in principle. Information retrieval means to mark documents in a set of candidate documents as relevant or non-relevant for some question, on the basis of the properties of the documents. For binary classification, the problem is to distinguish between the'positive' and'negative' instances from a dataset, based on the features of the instances. Hence, from an abstract point of view, information retrieval is a special case of binary classification, with the documents being instances, the document properties being features and'relevant' being translated as'positive'. In practice, however, the general concepts from binary classification are not always helpful for information retrieval applications. The fact that often the proportion of relevant documents in a set of documents subject to a search is small or even very small is only one of the reasons for information retrieval to be considered a field of research for its own. As a consequence, some performance measures for information retrieval methods differ from those in use for binary classifiers or are called by different names. Precision and recall are possibly the most popular performance measures(see Chapter 8 of Manning et al., 2008, for a list of performance measures) for information retrieval methods: - Precision is the proportion of documents (instances) that are truly relevant (positive) among those documents which have been predicted relevant (positive). The term precision is also commonly used (with the same meaning) in binary classification.

Being one of HubSpot's largest agency partners, we stay close to the pulse of the ever-changing platform. A few weeks back, they announced the keywords tool is being sunsetted at the end of May 2018. There was and still is a lot of backlash against this move because customers use the HubSpot Keywords tool for tracking their overall level of SEO success. Keyword rank tracking has long been a metric that marketers follow, and they will continue to do so. However, although HubSpot has continued to invest, grow, and update sales, marketing, and analytics tools within the platform, their keywords tool hasn't been updated since Google started encrypting keyword search data prior to 2013.

We prove a \emph{query complexity} lower bound for approximating the top $r$ dimensional eigenspace of a matrix. We consider an oracle model where, given a symmetric matrix $\mathbf{M} \in \mathbb{R}^{d \times d}$, an algorithm $\mathsf{Alg}$ is allowed to make $\mathsf{T}$ exact queries of the form $\mathsf{w}^{(i)} = \mathbf{M} \mathsf{v}^{(i)}$ for $i$ in $\{1,...,\mathsf{T}\}$, where $\mathsf{v}^{(i)}$ is drawn from a distribution which depends arbitrarily on the past queries and measurements $\{\mathsf{v}^{(j)},\mathsf{w}^{(i)}\}_{1 \le j \le i-1}$. We show that for every $\mathtt{gap} \in (0,1/2]$, there exists a distribution over matrices $\mathbf{M}$ for which 1) $\mathrm{gap}_r(\mathbf{M}) = \Omega(\mathtt{gap})$ (where $\mathrm{gap}_r(\mathbf{M})$ is the normalized gap between the $r$ and $r+1$-st largest-magnitude eigenvector of $\mathbf{M}$), and 2) any algorithm $\mathsf{Alg}$ which takes fewer than $\mathrm{const} \times \frac{r \log d}{\sqrt{\mathtt{gap}}}$ queries fails (with overwhelming probability) to identity a matrix $\widehat{\mathsf{V}} \in \mathbb{R}^{d \times r}$ with orthonormal columns for which $\langle \widehat{\mathsf{V}}, \mathbf{M} \widehat{\mathsf{V}}\rangle \ge (1 - \mathrm{const} \times \mathtt{gap})\sum_{i=1}^r \lambda_i(\mathbf{M})$. Our bound requires only that $d$ is a small polynomial in $1/\mathtt{gap}$ and $r$, and matches the upper bounds of Musco and Musco '15. Moreover, it establishes a strict separation between convex optimization and \emph{randomized}, "strict-saddle" non-convex optimization of which PCA is a canonical example: in the former, first-order methods can have dimension-free iteration complexity, whereas in PCA, the iteration complexity of gradient-based methods must necessarily grow with the dimension.

From the 1970s through today, there have been three waves of technology innovation in the Silicon Valley that have spawned many industries, including ours (online marketing). First, there were semiconductors, then personal computers, and most recently, the internet. Now we're on the cusp of the next wave of innovation, driven by advances in artificial intelligence (AI). Given that our industry exists thanks to these waves of innovation and that automation -- a top trend in PPC for 2017 -- can be driven by AI, how might things change for us during this next wave? And what risks do we face when we start to use far more automation without knowing the extent of its abilities?

Based on analytics, on-site and off-site considerations, do a health check on your website and create a baseline for ongoing website SEO performance. Find potential partners and influencers that can be utilized for link-building and PR efforts which supports the project's approach and timeline – Associate Find potential partners to publish content on their website with links to the client's website – Associate Significantly increase exposure of the client's business by encouraging targeted users to share and discuss related content We work continuously in order to stay ahead of the rapidly-changing SEO curve, providing regular, up-to-date training and professional development for all members of your team. We utilize proven, best practices that maximize short and long term results, creating quality content and structuring web pages in an efficient and effective manner. Our team of SEO specialists is defined by persistence and tenacity so there will never be wasted potential opportunities.

In this post we will focus on configuring the elasticsearch bit. I have chosen the Wikipedia people dump for the dataset. This is the wiki pages of a subset of people on Wikipedia. This dataset consists of three columns – URI, name, text. As the column names suggest, URI is the actual wiki link to that person's page, name is the person's name.

This paper will explore the use of autoencoders for semantic hashing in the context of Information Retrieval. This paper will summarize how to efficiently train an autoencoder in order to create meaningful and low-dimensional encodings of data. This paper will demonstrate how computing and storing the closest encodings to an input query can help speed up search time and improve the quality of our search results. The novel contributions of this paper involve using the representation of the data learned by an auto-encoder in order to augment our search query in various ways. I present and evaluate the new gradient search augmentation (GSA) approach, as well as the more well-known pseudo-relevance-feedback (PRF) adjustment. I find that GSA helps to improve the performance of the TF-IDF based information retrieval system, and PRF combined with GSA works best overall for the systems compared in this paper.

You might have heard about those ATMs that use facial recognition instead of cards and PIN numbers for authentication. You might also have seen on the news a smart security algorithm that helps police identify suspects and cracks criminal cases. Artificial intelligence (AI), the wiz behind these advanced technologies, is permeating our daily lives--everything from financial services to public safety to healthcare and transportation. YITU Technology, one of China's front-running AI startups, has developed solutions that help solve real-world problems. YITU now has the ability to enable accurate facial recognition with a large database of over 1 billion faces in just one second, and their technology has in fact assisted Chinese law enforcement in criminal investigations.

On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance.