Hi Wagar,

i.context-aware RS

ii. privacy preserving RS

iii. inclusion of user input in RS

iv. Deep learning, soft computing algorithms for developing RS

Wagner, try this company - PST - Psychology Software Tools , I used their software called MEL2 quite some time ago to do exactly what you are doing - or almost - in my case position within the video was not recorded - just when a choice was made via mouse click. Today they most likely have better tools - they also make other toys in which you may have some interest.

If they no longer make that software, they might be able to point you in the proper direction.

https://pstnet.com/

One of the trending domains where RS's are being constantly applied and redefined is as follows,

• Recommendation system for CV's. Based on the candidates applying for a particular job role, employers or Talent acquisition managers can get personalized recommendations for candidates fit for an interview for the open position. There are tons of third party tools that large organizations use for the same purpose.

The field of recommender systems, especially in academia, has been focusing mainly on the accuracy-related measures of successful recommendation. Many people have worked on things like how to improve accuracy of the recommendations by a tiny bit while the real effect of this improvement is really not clear. However, the real-world industrial recommender systems have proved that this over-emphasis on accuracy is wrong and there are other measures that have a better correlation with the success of a business. Things like diversity, novelty, long-tail recommendations, and removing biases. In addition, the new notion of multi-stakeholder recommendation has opened a lot of doors for further research. For example, taking into account different stakeholders' needs of a given recommender system is critical for the long-term success of the business. Think about Spotify, for instance. Spotify's success depends on the satisfaction of both the listeners and the artists. Therefore focusing only on the listeners may not necessarily lead to long-term success. To learn about the research directions in Multistakeholder recommendation I suggest our paper at UMUAI journal which can be a great read for someone who is looking for novel ideas.

https://link.springer.com/article/10.1007/s11257-019-09256-1#:~:text=Definition%202,over%20the%20full%20user%20population.