With PAQATO, online retailers offer their customers an outstanding shopping experience after the checkout and ensure maximum satisfaction. Personalized shipping notifications and a tracking page in their own store turn customers into loyal fans. Through proactive multi-channel communication, online stores exploit cross-selling potential during the shipping process and collect strong customer reviews. At the same time, smart shipment controlling allows online retailers to maintain a full overview of their shipping processes and reduce their service costs with transparent data. Today, over 1,000 e-commerce companies such as OTTO, Peek & Cloppenburg, ROSE Bikes and KoRo are already delighting their customers with outstanding after-sales experiences.
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Based on our record, Taste seems to be more popular. It has been mentiond 4 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Try taste.io, you cannot find users, but it will suggest you movies that people with similar tastes liked. Source: almost 2 years ago
On a social website (taste.io) I read a comment complaining about ‘bi- and homophobia sprinkled throughout [Elementary]’. The site doesn’t allow to react to comments so I couldn’t ask the person, but their comment got me thinking and I would like to hear people’s opinion: Do you think the show has some problematic moments in regards to lgbt+ representation and if yes, can you provide concrete examples? Source: about 2 years ago
It's John from taste.io, I think it depends on the method you want to use and where you're able to retrieve data to train the model. With a short amount of time and limited resources, you won't have the luxury of creating a collaborative filtering model....content-filtering is possible if you can also be resourceful with APIs + build crawlers. But, the results might be mediocre...meaning, the recommendations... Source: over 2 years ago
I have been asked to build a recommender system for TV shows at large scale, meaning thousands of users across the entire libraries of services like Netflix, Hulu and Amazon Prime. Something like taste.io but completely focussed on TV shows and not movies. My main concern is the complexity of this project, I have read up on recommender systems, and they seem fairly straightforward, its the scale that scares me. Source: over 2 years ago
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