Marketers evaluate recommender systems not on their algorithms but on how well the vendor's expertise and interfaces will support achieving business goals. Driven by a business model that pays based on recommendation success, vendors guide clients through continuous optimization of recommendations. While recommender technology is mature, the solutions and market are still young. As a result, solutions are not fully integrated with other business systems and technology platforms. While the market is retail-focused today, interest and vendor offerings are rapidly expanding to other areas.
If you've ever shopped online (*cough* Amazon *cough*), you've probably experienced the "vacuum cleaner effect". You carefully buy one expensive item (e.g. a vacuum cleaner) and then you receive dozens of recommendations for other vacuum cleaners to buy: by email, everywhere on the retailer's website, or sometimes in the ads you see on other websites. In other terms, Amazon is a 1 trillion dollar company that employs hundreds of data scientists and is incapable of understanding that if you bought an expensive appliance, buying another one of the same category in the next weeks is what you're *least* likely to do! But let's think about the problem for a second. Suggesting item that are similar to what you just bought is actually the core feature of recommendation algorithms!
Of course we have all heard about machine learning and recommendation engines in big business ecommerce. For quite some time, massive ecommerce businesses like Netflix, Amazon, and Ebay have been leveraging the power of data science to improve customer service and boost sales. Where once this technology was cost-prohibitive to all but the major players, recently things have changed. Thanks to multi-channel ecommerce platforms like Shopify, and the developers who are building custom machine learning add-ons, now mom and pop online businesses get the chance to infuse their operations with the power of data science. In this article I introduce how machine learning algorithms work to produce recommendation systems for small business ecommerce.
If you're roaming the internet, you might run across recommendation engines. Some offer products or services you'd actually want to use, but does interacting with them harm your privacy even more? Researchers developed an algorithm to reveal how much info you're handing over when you click around, essentially picking out which parts of a recommendation engine are and aren't safe for privacy-minded users. The team fed their algorithm datasets from recommendation engines Movielens and Jester. Essentially, their math weighs the value users get by clicking against the personal info they're disclosing, and points out when users would be handing over too much information for too little effect.
House and Senate leaders never brought forth a final proposal for changes to school funding, after the report they commissioned suggested increasing state spending and redistributing money in ways that could have forced about 30 property-rich districts to raise property taxes. Many lawmakers rejected that call to increase local tax contributions, but said they wanted to enact other recommendations. These recommendations could have created a need for at least $120 million of additional state spending.