>> Steve Jobs — \u201ca lot of times, people don\u2019t know what they want until you show it to them\u201d[3]<\/sup> <<<\/strong><\/p>\n The most well-known types of recommendation engines are four[4]<\/sup> and use data and algorithms slightly differently:<\/p>\n Truth is, that companies are utilizing a hybrid of all of these kinds[5]<\/sup>\u00a0and implementing machine and deep learning to continuously adjust the different weights of each type and characteristic to improve the relevance of the results.<\/p>\n Recommendations translation to UX\/UI<\/span><\/p>\n So the magic is all about recommending the exact accurate promotion at the right time? Well\u2026 that is part of the story, but not all.<\/p>\n Of course, recommendation accuracy is important, but it is not the only key. The User interface in the delivery of these recommendations is even more powerful. For example, recognizing that there are some items that you buy once versus others that you consume repetitively could be useful not to damage the customer experience.<\/p>\n <\/p>\n Sometimes, to solve for some of these issues, explanations on how these recommendations are being provided can help increase trust among users: tags like \u201cPeople who bought this, also bought\u2026\u201d or \u201cPeople who watched this, also enjoyed\u2026\u201d are effective in enhancing transparency.<\/p>\n Challenges ahead:<\/span><\/p>\n Of course, these recommendation engines are far from perfect, and billionare investments are being destined in dramatically evolving these valuable mechanisms. Still, there also some short-term challenges (many that have been discussed during our course) that should be addressed:<\/p>\n 1) \u201cGarbage in, garbage out\u201d<\/strong>: Because all these algorithms rely on data, the availability of good quality big data is the key to adding value to the customer and the organizations. Poor quality data might arise from, for example, redundant applications and databases, or data storage costs difficulting access and use.<\/p>\n 2) Sensitivity and Privacy:<\/strong> Given many of the current challenges with data sharing and handling, companies should beware of how much care they are putting in safe handling individual and organizational privacy and data security. Creeping out users, by exposing they are being tracked or showing that they can access they facebook friends profiles, might backlash by losing customer willingness to share other types or even more relevant data in the future.<\/p>\n Other Dimensions:<\/span><\/p>\n Using data is not exclusive the particular tool (recommendation engines) or platform (website, app). In fact, Amazon is experimenting with others things like:<\/p>\n Dynamic Pricing<\/strong>: Amazon.com dynamic pricing system monitors competing prices, alerting and informing Amazon’s pricing every 15s, which has also resulted in increased sales to Amazon by allowing the company to offer competitive prices to customers (even in special events like x-mas sales)<\/p>\n Interaction with recommenders, the AI evolution:<\/strong> The way that we will interact with these recommendation engines is eveolving, and will be increasingly important in the future. How long do you believe it will be for Alexa to develop a Q&A interface that will result in the perfect match for your very urgent need? My guess is not very long.<\/p>\n The future space for recommendation systems is still unknowned, but for sure looks to be headed toward a totally customized environment for each user, which will be unlike anything we\u2019ve ever experienced[6]<\/sup>.<\/p>\n <\/p>\n Sources:<\/p>\n [1] https:\/\/www.martechadvisor.com\/articles\/customer-experience-2\/recommendation-engines-how-amazon-and-netflix-are-winning-the-personalization-battle\/<\/a><\/p>\n [2] https:\/\/www.mckinsey.com\/industries\/retail\/our-insights\/how-retailers-can-keep-up-with-consumers<\/a><\/p>\n [3] https:\/\/www.forbes.com\/sites\/chunkamui\/2011\/10\/17\/five-dangerous-lessons-to-learn-from-steve-jobs\/#4b97f4263a95<\/a><\/p>\n [4] https:\/\/yanirseroussi.com\/2015\/10\/02\/the-wonderful-world-of-recommender-systems\/<\/a><\/p>\n [5] https:\/\/medium.com\/netflix-techblog\/learning-a-personalized-homepage-aa8ec670359a<\/a><\/p>\n [6] https:\/\/techcrunch.com\/2015\/09\/28\/the-evolving-landscape-of-recommendation-systems\/<\/a><\/p>\n Other sources of reference:<\/p>\n https:\/\/hbr.org\/2017\/08\/great-digital-companies-build-great-recommendation-engines<\/a><\/p>\n https:\/\/analyticsindiamag.com\/startup-week-expertrec-disrupting-search-recommendation-using-machine-learning\/<\/a><\/p>\n https:\/\/www.kdnuggets.com\/2015\/10\/big-data-recommendation-systems-change-lives.html<\/a><\/p>\n https:\/\/www.techemergence.com\/use-cases-recommendation-systems\/<\/a><\/p>\n https:\/\/www.techemergence.com\/deciphering-the-discovery-engines-that-decipher-our-digital-wants-and-needs-a-conversation-with-raefer-gabriel\/<\/a><\/p>\n http:\/\/infolab.stanford.edu\/~ullman\/mmds\/ch9.pdf<\/a><\/p>\n https:\/\/www.wired.com\/2015\/04\/now-anyone-can-tap-ai-behind-amazons-recommendations\/<\/a><\/p>\n https:\/\/link.springer.com\/content\/pdf\/10.1007%2Fs12525-016-0219-0.pdf<\/a><\/p>\n https:\/\/www.cs.umd.edu\/~samir\/498\/Amazon-Recommendations.pdf<\/a><\/p>\n\n
![\"\"](\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2018\/04\/AMAZON-POST.png\")
<\/a><\/p>\n
\n