Collaborative filtering (CF)[1] based on the alternating least squares (ALS) technique[2] is another algorithm used to generate recommendations. It produces automatic predictions (filtering) about the interests of a user by collecting preferences from many other users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than a randomly chosen person. This algorithm gained a lot of traction in the data science community after it was used by the team winner of the Netflix Prize. The CF algorithm has also been implemented in Spark MLlib[3] with the aim to address fast execution on very large datasets.

The practice of collecting basic demographic information from customers to create a successful business marketing strategy is one of the past. In recent times, there has been a major shift in the way that businesses interact with their customers. The digital space has spread so far and wide that it has had a lasting influence on virtually everything we do. As a result, the conventional approaches to marketing that were prevalent even as early as a few years ago are considered severely ineffective today. The rapidly growing popularity of Big Data means that marketers need to embrace sophisticated approaches to processes and perform in-depth analysis of customer data, preferably in real-time.

Chatbots and AI assistants need further development to reach the point where they can fully understand the nuances of language and engage in real conversations with people. But good design can help organizations overcome the limitations of existing conversational AI bots, according to developers at companies on the leading edge of the trend. "I think it's a myth that we'll have to wait until the best AI materializes to improve assistants," said Cathy Pearl, head of conversation design outreach at Google. "Actually, we can do a lot now with good design principles and a little technology that we have now." Conversational AI was one of the big discussion topics at the 2019 Re•Work Deep Learning Summit in San Francisco.

Despite the overall tone, which was a tad too optimistic for my taste, I found the video insightful and recommend a watch. OpenAI's new algorithm learns from mistakes

Large-scale industrial recommender systems are usually confronted with computational problems due to the enormous corpus size. To retrieve and recommend the most relevant items to users under response time limits, resorting to an efficient index structure is an effective and practical solution. Tree-based Deep Model (TDM) for recommendation \cite{zhu2018learning} greatly improves recommendation accuracy using tree index. By indexing items in a tree hierarchy and training a user-node preference prediction model satisfying a max-heap like property in the tree, TDM provides logarithmic computational complexity w.r.t. the corpus size, enabling the use of arbitrary advanced models in candidate retrieval and recommendation. In tree-based recommendation methods, the quality of both the tree index and the trained user preference prediction model determines the recommendation accuracy for the most part. We argue that the learning of tree index and user preference model has interdependence. Our purpose, in this paper, is to develop a method to jointly learn the index structure and user preference prediction model. In our proposed joint optimization framework, the learning of index and user preference prediction model are carried out under a unified performance measure. Besides, we come up with a novel hierarchical user preference representation utilizing the tree index hierarchy. Experimental evaluations with two large-scale real-world datasets show that the proposed method improves recommendation accuracy significantly. Online A/B test results at Taobao display advertising also demonstrate the effectiveness of the proposed method in production environments.

Amazon, Apple and Google voice assistants and smart TVs could find out if a person is cheating on their partner, a data expert has claimed. Smart gadgets, which are used by one in ten people around the UK, can harvest enough data to work out the dynamics of a relationship, they say. They have the potential to record saucy conversations and analyse location data to discover secret affairs. The virtual assistants can show when occupants are in a building, or for example, share a bedroom, by using sensor logs and smart meters. There is already ambiguity when it comes to these companies privacy policies and how they collect and use people's data.

A few weeks after being pulled because of connectivity issues, Amazon's Echo Wall Clock is once again available. The clock can once again be purchased for $29.99 from Amazon. A spokesperson for Amazon confirmed the availability of the clock to Engadget. The company also said that a software update will be made available for customers who have already purchased the clock. That update will be received automatically when the clock is connected to an Echo device.

AI is a milestone in the software industry. However, it is creating uncertainty amongst the manual testers with respect to their job. Manual testing is one of the oldest and traditional methods of testing. However, will software testing be the same way as today is the biggest question? The answer is, it will not be the same, but then you need manual intervention to design testing strategies.

A quick Google search reveals there are dozens of online dating sites to consider using when looking for a partner. But, even though online dating offers so many options that are mere clicks away, most people won't keep using these sites if they aren't fruitful. With that in mind, some companies have started using big data analysis tools to improve the likelihood of good matches between users. When signing up for a dating site, people must provide basic information about themselves, such as their ages, locations, and genders. Additionally, they're usually encouraged to give other details, like whether they smoke, what they look for in partners, and their primary interests.

If you had visited the Cambridge University Library in the late 1990s, you might have observed a skinny young man, his face illuminated by the glow of a laptop screen, camping out in the stacks. William Tunstall- Pedoe had wrapped up his studies in computer science several years earlier, but he still relished the musty aroma of old paper, the feeling of books pressing in from every side. The library received a copy of nearly everything published in the United Kingdom, and the sheer volume of information--5 million books and 1.2 million periodicals--inspired him. It was around this time, of course, that another vast repository of knowledge--the internet--was taking shape. Google, with its famous mission statement "to organize the world's information and make it universally accessible and useful," was proudly stepping into its role as librarian to the planet.