Could open source models be the key to affordable and efficient news recommendations? @helloheld's article explores that while LLMs have strong predictive power, they come with high costs and slow speeds.
#LLM
https://t.co/CXNYkugBhW
Optimizing recommendation engines is no easy feat. With costs high and response times slow, LLMs face scalability challenges in news platforms. However, refining prompts and using additional user data could unlock significant improvements. Read @helloheld's latest article now.
#LLM
https://t.co/t7poHdeTM5
What's the best way to evaluate news recommendations systems? @helloheld walks us through a mixed-methods approach he's leveraged in his work @derspiegel.
https://t.co/oNmCHHIQk1
Combining reader feedback from surveys with behavioral click data, @helloheld presents a mixed-methods approach to evaluate the performance of news recommendation systems. https://t.co/oNmCHHIQk1
13,129km. No limits.* The Bundesautobahn.
Requested, inspired, and made possible by @_ansgar.
Tried to make the lines into illuminated neon tubes but I misspelled radius and woke up to an error. 😠
* Length as of 2021. Also mostly no limits.
#dataviz#rstats