{"id":77918,"date":"2016-09-12T20:53:19","date_gmt":"2016-09-12T18:53:19","guid":{"rendered":"http:\/\/www.myza.co.za\/tastemonster-cape-town-based-software-company-breaks-the-mould-on-machine-learning\/"},"modified":"2016-09-12T20:53:19","modified_gmt":"2016-09-12T18:53:19","slug":"tastemonster-cape-town-based-software-company-breaks-the-mould-on-machine-learning","status":"publish","type":"post","link":"https:\/\/www.myza.co.za\/press\/tastemonster-cape-town-based-software-company-breaks-the-mould-on-machine-learning\/","title":{"rendered":"TasteMonster: Cape Town based software company breaks the mould on machine learning"},"content":{"rendered":"

TasteMonster: Cape Town based software company breaks the mould on machine learning<\/a>: <\/p>\n

\"TasteMonster1\"<\/p>\n

TasteMonster Photo: Julia Ledingham<\/p>\n<\/div>\n

There is a new recommendation system on the net and it\u2019s fantastic.\u00a0The unique intuitive system for streaming sites enables users to access tailored predictions and match shows and movies in real time for you and your friends.<\/p>\n

Cape Town based developer Rick De Villiers has finally released his new predictions and recommendations system TasteMonster, for streaming sites such as Netflix, Amazon Prime, Showmax and iTunes.\u00a0The site enables you to gain accurate, timely recommendations and predictions matched to your viewing preferences.<\/p>\n

There is a growing trend worldwide for people to be choosing streaming media providers over traditional satellite and cable television. But, when faced with so much choice, how do you know what to watch without trawling through each streaming channel? Sure you can look at critic sites like Rotten Tomatoes, but these sites aggregate ratings and provide you with a general consensus. This would work if you considered yourself to be statistically average, but chances are that you are not.<\/p>\n

Software developer Rick de Villiers explains: \u00a0\u201cIf you have experienced Netflix or Itunes recommendations you\u2019ll know how irrelevant they can be.\u00a0 Most have fallen back to simple genre-based or associative suggestions, for instance I really enjoy Star Wars but dislike Star Trek.\u00a0 The problem here is that my taste in Sci-Fi movies is quite selective and simple recommendations just don\u2019t work for me and are not accurate enough<\/em>.\u201d<\/p>\n

How do we create the perfect solution?<\/strong><\/p>\n

The natural human method for discovering recommendations is to take suggestions from people that we trust.\u00a0 We\u2019ve all had that experience where we are socialising with friends and someone says \u201cOh wow have you seen that movie\u201d.\u00a0 The problem here is trust.\u00a0 We don\u2019t trust too many people with their recommendations.<\/p>\n

Imagine if a system could expose me to 1000 people around the world who I trusted completely, imagine all the great discoveries that I\u2019d make.\u00a0 That would be powerful. We know that collaborative recommenders are not new.\u00a0 The ACM RecSys conference has met annual for a decade with players like Spotify, Netflix, Amazon and Google all attending.\u00a0 But recommendation algorithms are all about compromise.\u00a0 Real-time systems are inaccurate and accurate systems require days of processing to produce results.<\/p>\n

So if we were to make the ideal collaborative recommender, here\u2019s what we\u2019d want:<\/strong><\/p>\n