Recommendation System in R Recommender systems are used to predict the best products to offer to customers. These babies have become extremely popular in virtually every single industry, helping customers find products they'll like. Most people are familiar with the idea, but nearly everyone is exposed to several forms of personalized offers and recommendations each day (Google search ads being among the biggest source). Building recommendation systems is part science, part art, and many have become extremely sophisticated. This is a post about building recommender systems in R. UPDATE: We used the beer / product recommender for a talk at PyData Boston in July. Beer Dataset "Respect Beer." - BeerAdvocate.com For this example, we'll use data from Beer Advocate, a community of beer enthusiasts and industry professionals dedicated to supporting and promoting beer. Each record is composed of a beer's name, brewery, and metadata like style and ABV etc., along with ratings provided by reviewers. Formatting the Data
Types of Charts and Graphs: Choosing the Best Chart You’ve got your data, you’ve made some sense of it, and now it is time to communicate your results. Great! This article will provide examples of many types of charts and graphs and explain how to pick the best one for your data depending on the message you want to convey. Choosing a type of chart depends first and foremost on what kind of data you have and what you want to express. 1. Bar Chart Comparing Presidents and Executive Orders, Source: I Love Charts Venn diagrams are especially useful to show relationships. 2. Column Histogram Chart scatter plot chart Scatter Charts in MS Excel Word clouds are an interesting way to visualize the frequency distribution of words with textual data. Word Cloud Oscars 2011 3. Stock Candlestick Chart Here is a good article if you want to learn more about stock market charts. If you have a few extra minutes, here’s an incredible video of Hans Rosling showing charts in motion, demonstrating both relationships and trends all at once. 4. Doughnut Charts 5. 6.
Understanding Full-Text Indexing in SQL Server Full-text indexing in SQL Server has been quietly improving as Microsoft have worked on it over the last few years, making this a good time to look at what it offers. Who better to give us that look than Robert Sheldon, in the first of a series. The most commonly used indexes in a SQL Server database are clustered and nonclustered indexes that are organized in a B-tree structure. You can create these types of indexes on most columns in a table or a view, except those columns configured with large object (LOB) data types, such as text and varchar(max). Although this limitation is not a problem in many cases, there will be times when you’ll want to query such column types. Full-text search refers to the functionality in SQL Server that supports full-text queries against character-based data. In this article, I explain how to implement full-text indexing in your SQL Server 2005 or 2008 database, and I provide a number of examples to demonstrate how this is done. ORDER BY lcid about after all
t sql - SQL Server Full-Text Search: combining proximity term and thesaurus I do not believe that is a supported feature of SQL Server full text searching. That would require a wildcard resolution to words and then a thesaurus lookup of each matching word to gather the thesaurus terms. This basically maps to a pretty complex query: some one of a group of prefixed words is very near to some one of another group of prefixed words which all then go through a thesaurus lookup to provide even more words. Based on previous experience, that is just not supported. (I see online that you have asked this elsewhere in the last few months, but without any answers, so I hope that this helps.) I believe that you can create something useful for your query, but it probably requires externalizing the thesaurus entries by doing something like the following: So if you load the tables with the candidate thesaurus entries and make the OR connections in the code, it should work just fine.
Introduction to Information Retrieval This is the companion website for the following book. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008. You can order this book at CUP, at your local bookstore or on the internet. The book aims to provide a modern approach to information retrieval from a computer science perspective. We'd be pleased to get feedback about how this book works out as a textbook, what is missing, or covered in too much detail, or what is simply wrong. Online resources Apart from small differences (mainly concerning copy editing and figures), the online editions should have the same content as the print edition. The following materials are available online. Information retrieval resources A list of information retrieval resources is also available. Introduction to Information Retrieval: Table of Contents
Export A Large Access Table/Query To Excel ~ My Engineering World The previous days I had to update a large Access database. I had a large table – around 1.000.000 records/rows – that I had to export to an Excel workbook, perform some calculations/corrections and import the table back to the database. So, you might wonder what was the problem, right? A) A simple copy paste using the Clipboard. A table that contains 1.000.000 records divided by 65.000 gives around 16 groups. B) I tried the export feature of Access, but, although the Excel file was created, no data was inserted into the spreadsheet. Being disappointed by my previous attempts I decided to try a VBA solution. VBA code The following lines of code constitute the aforementioned VBA function (DataToExcel), plus a small sub that make use of the function (Test). Option Compare Database Option Explicit Sub Test() 'Change the names according to your own needs. Bear in mind that you cannot import into an Excel worksheet more than 1048576 rows. How to use it A much simpler solution
Kepion Partner Program | Kepion Solution Kepion works closely with all partners as an extension of our team. We believe in partners providing the best expertise needed around what the customer needs to compete and grow. Kepion Partners represent a diverse range of solutions, services and geographies. We have aligned with leading technology and implementation partners that have the right expertise and experience. Together we build sustainable and faster BI & Performance Management solutions for their business. With the Kepion Partner Program, your company benefits from the most flexible BI and Performance Management solution enabled for you to build solutions around the way your customers work. Kepion Partner Types Alliance Partners Kepion Alliance Partners are here to help you determine the right solution to fit your business' performance management needs. OEM Partners Kepion OEM Partners embed our powerful planning and reporting capabilities into their own application. Reseller Partners Technology Partners Partner Benefits
Blockspring for Google Sheets – Blockspring We are excited to announce that all 1000+ functions on Blockspring can now be run from your spreadsheet. You'll be able to create interactive data visualizations, run algorithms, pull data from the web, automate tweets and emails, call APIs, and more. In a nutshell, you get the full power of programming from the comfort of a spreadsheet. To get started, install the Blockspring for Google Sheets add-on from the Chrome Store. Example use cases Open Data - Which Chicago hotels are hot spots for... prostitution busts? Data Visualization - Summarize recent news. APIs - Use FullContact to find contact info for TechCrunch writers. Sales - Merge customer lists. Marketing - Customer segmentation/clustering in a jiffy. Finance - When in the week do websites get the most traffic? A bit of background Within the Blockspring community, we've seen developers collaborate across programming languages like never before. The results are exciting.