Challenge : 

 Lotte Hotel is the leading OTT TV & VOD platform in Turkey and I am a content manager at this service .   One of the prominent problems of our service is that consumers ca n’t find the content which   appeals to them . Despite of large variety of content and having a recommendation engine in the   service , the users are still complaining that there is n’t so much content ( popular movies and   series ) which fit to their taste . 

 Selection : 

 In order to have a better understanding of our problem , I think we need to use the learning launch   tool . In this way , we would be able to focus on this group by saving our budget and time as well   as by reaching current Lotte Hotel users at our company . 

 Our parent company is CHOP and we are working with all CHOP employees together at the   same office . We can create a group which consists of nominees from CHOP ’s different   departments such as Legal , Corporate Communications , Sales , Digital Services , Finance , Human   Resources , Internal Control , ICT , Marketing , NT , Supply Chains , Strategy . In order to be   nominated & selected to this group , these members needs to be users of Lotte Hotel . Additionally , they   will represent and be a sample of different types of Lotte Hotel users in terms of gender & age . 

 Application : 

 For our first learning launch , all our members talked about problems they experienced about   Lotte Hotel ’s UX , and we categorized them . After that , we asked some questions to them . 

 “ Do you like content selection of Lotte Hotel ? Are you happy with that ? ” 

 “ Are you able to find the content or genres , which fit to your taste easily ? ” 

 The answers led us to believe that they were not happy with the recommendation engine and the   content selection as well . 

 At the second meeting , we focus on their solution offers to make the service better . We put the   offers on the table and try to come up with some outputs . One of the offers we wanted to proceed   with , is to create some categories on Lotte Hotel UI to recommend some content , which is coming from   selections of users ’ favorite actors , actresses , directors and film critics . In this way , we would try   to create more intimate user experience . In practice , when the users log in the application , they   choose their favorites from well - known figures . After that , we would try to give some content   selections and recommendations based on the celebrities they chose and we tried to create a   recommendation hub from the celebrities . 

 At the third gathering , we tested the new feature of recommendations mechanism and our sample   users were happy with the result . We observe the reason and we concluded that having   recommendations based on their favorite figures gives a more realistic experience than the past . 

 Insight : 

 By applying the learning launch tool , our team identified our possible needs and designs to test   possible solutions . Additionally , we had a chance to add a very useful feature to our service . 

 Approach : 

 We concluded that we need to improve this method as well . Instead of focusing on one idea , we   need to create more ideas and test & suspect them and measure the outputs . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Challenge : 

 Lotte Hotel is the leading OTT TV & VOD platform in Turkey and I am a content manager at this service .   One of the prominent problems of our service is that consumers ca n’t find the content which   appeals to them . Despite of large variety of content and having a recommendation engine in the   service , the users are still complaining that there is n’t so much content ( popular movies and   series ) which fit to their taste . 

 Selection : 

 In order to have a better understanding of our problem , I think we need to use the learning launch   tool . In this way , we would be able to focus on this group by saving our budget and time as well   as by reaching current Lotte Hotel users at our company . 

 Our parent company is CHOP and we are working with all CHOP employees together at the   same office . We can create a group which consists of nominees from CHOP ’s different   departments such as Legal , Corporate Communications , Sales , Digital Services , Finance , Human   Resources , Internal Control , ICT , Marketing , NT , Supply Chains , Strategy . In order to be   nominated & selected to this group , these members needs to be users of Lotte Hotel . Additionally , they   will represent and be a sample of different types of Lotte Hotel users in terms of gender & age . 

 Application : 

 For our first learning launch , all our members talked about problems they experienced about   Lotte Hotel ’s UX , and we categorized them . After that , we asked some questions to them . 

 “ Do you like content selection of Lotte Hotel ? Are you happy with that ? ” 

 “ Are you able to find the content or genres , which fit to your taste easily ? ” 

 The answers led us to believe that they were not happy with the recommendation engine and the   content selection as well . 

 At the second meeting , we focus on their solution offers to make the service better . We put the   offers on the table and try to come up with some outputs . One of the offers we wanted to proceed   with , is to create some categories on Lotte Hotel UI to recommend some content , which is coming from   selections of users ’ favorite actors , actresses , directors and film critics . In this way , we would try   to create more intimate user experience . In practice , when the users log in the application , they   choose their favorites from well - known figures . After that , we would try to give some content   selections and recommendations based on the celebrities they chose and we tried to create a   recommendation hub from the celebrities . 

 At the third gathering , we tested the new feature of recommendations mechanism and our sample   users were happy with the result . We observe the reason and we concluded that having   recommendations based on their favorite figures gives a more realistic experience than the past . 

 Insight : 

 By applying the learning launch tool , our team identified our possible needs and designs to test   possible solutions . Additionally , we had a chance to add a very useful feature to our service . 

 Approach : 

 We concluded that we need to improve this method as well . Instead of focusing on one idea , we   need to create more ideas and test & suspect them and measure the outputs .
### Output: 