Whether Father's Day, Mother's Day, a birthday or simply "just because," buying gifts can feel like counting grains of sand -- i.e., it's not easy. Meanwhile, there are 250,ooo -- 300,000 e-commerce companies in the U.S. all vying for the attention of shoppers. How are consumers possibly expected to decide where to spend their hard-earned money? And how can retailer s create a more personalized shopping experience, rather than facing the same demise as the 6,700 retail locations that closed their doors in 2017? In short, the retail sector is in a sticky wicket.

Artificial intelligence is no longer a far-off concept; today AI and machine learning part of our everyday lives. For instance, Siri, Alexa and Google Home use neural networks for natural language processing to analyze the human voice and respond accordingly. Nest Learning Thermostat uses AI algorithms to control the climate in your home, while Nest Cam uses AI and facial recognition to reduce false alarms. The security screener at the airport uses machine learning to augment human intelligence. Netflix's online learning serves the perfect movie based on your previous selections.

Matchmaking has been around since the late 1950s. From punch card questionnaires to now finding your significant other through an app, it has evolved with technology. Online dating sites provide singles with options based on shared interests to race preference. It has been said that online dating will be a thing of the past, but companies like Match.com which launched in 1995 and Eharmony.com in 2000 prove that there is longevity in this business.

Voice shopping using smart speakers and smartphone apps is starting to gain traction among consumers, opening up a new "conversational commerce" channel and potentially disrupting the retail sector. Devices such as Amazon's Alexa-powered speakers and Google Home, which use artificial intelligence (AI) to respond to voice commands, are offering new choices to consumers who are looking for more convenient ways to order goods and services. Voice shopping is expected to jump to US$40 billion (S$54 billion) annually in 2022 in the United States, from US$2 billion today, according to a survey this year by OC&C Strategy Consultants. "People are liking the convenience and natural interaction of using voice," said Ms Victoria Petrock of the research firm eMarketer. "Computing in general is moving more toward voice interface because the technology is more affordable, and people are responding well because they don't have to type."

Here are five things in technology that happened this past week and how they affect your business. Facebook has announced the global rollout of a new policy that will now let users file a complaint about businesses they've had a problem with if they bought something after clicking on one of their ads. If enough people complain about a business, it could lead Facebook to ban the company from running ads. The new policy is meant to help Facebook fight back against advertising abuse on its platform and trying to prevent "bad shopping experiences," which can cost customers and make them frustrated with Facebook, too. The good news for your business is that if you're on the up and up, do a good job, and provide good products and service you'll be fine.

Machine learning, data science, and data analytics or scientists are emerging fields growing into various sub-fields, helping companies improve their efficiency and performance at certain stages during the operations and services. Hence, understanding these technologies is very important to realize their right use and benefits into various sectors. So, here we have discussed what data science is and what is machine learning with few sets of examples. Data science is a term used for dealing with big data that includes data collection, cleansing, preparation, and analysis for various purposes. A data scientist collects data from multiple sources and after analysis, applies into predictive analysis or machine learning and sentiment analysis to extract the critical information from the data sets.

If you're already resentful of online dating culture and how it turned finding companionship into a game, you may not be quite ready for this: Crown, a new dating app that actually turns getting matches into a game. Crown is the latest project to launch from Match Group, the operator of a number of dating sites and apps including Match, Tinder, Plenty of Fish, OK Cupid, and others. The app was thought up by Match Product Manager Patricia Parker, who understands first-hand both the challenges and the benefits of online dating – Parker met her husband online, so has direct experience in the world of online dating. Crown won Match Group's internal "ideathon," and was then developed in-house by a team of millennial women, with a goal of serving women's needs in particular. The main problem Crown is trying to solve is the cognitive overload of using dating apps.

The dominant, state-of-the-art collaborative filtering (CF) methods today mainly comprises neural models. In these models, deep neural networks, e.g.., multi-layered perceptrons (MLP), are often used to model nonlinear relationships between user and item representations. As opposed to shallow models (e.g., factorization-based models), deep models generally provide a greater extent of expressiveness, albeit at the expense of impaired/restricted information flow. Consequently, the performance of most neural CF models plateaus at 3-4 layers, with performance stagnating or even degrading when increasing the model depth. As such, the question of how to train really deep networks in the context of CF remains unclear. To this end, this paper proposes a new technique that enables training neural CF models all the way up to 20 layers and beyond. Our proposed approach utilizes a new hierarchical self-attention mechanism that learns introspective intra-feature similarity across all the hidden layers of a standard MLP model. All in all, our proposed architecture, SA-NCF (Self-Attentive Neural Collaborative Filtering) is a densely connected self-matching model that can be trained up to 24 layers without plateau-ing, achieving wide performance margins against its competitors. On several popular benchmark datasets, our proposed architecture achieves up to an absolute improvement of 23%-58% and 1.3x to 2.8x fold improvement in terms of nDCG@10 and Hit Ratio (HR@10) scores over several strong neural CF baselines.

But in the last few years, Alexa and Siri have moved in. With this invasion of AI assistants comes incredible command over technology: Don't set a timer or check the weather; ask. And don't spring for that spinning, oak coffee-table caddy to house your obnoxious array of remotes. With the latest TV innovations, all you'll need to say is "Alexa, play'Dawson's Creek'" to immediately power on your TV, streaming device and soundbar, search for the show (now on Hulu), and start where it you left off. "The control and convenience that comes from the next generation of voice is really going to enhance the TV experience," said John Taylor, senior vice president of LG Electronics and friend of Eugene Polley and Dr. Robert Adler, engineers who invented Zenith's original 1950 remote.

Jefferson Graham tells why the Sony RX10IV is his favorite all-around camera on TalkingTech. Father's Day is almost here again, and if dad is a gear head, he'd love nothing more than new tech as his Sunday present. I'm part of that cliche, but if you're thinking of buying me something, I probably already have it. I can't wait for holidays to add to my collection. But I do have several ideas for the father in your life.