[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.] #  @xamat Xavier(Xavi) Amatriain Xavier(Xavi) Amatriain posts on X about prague, age of, the early, acm the most. They currently have XXXXXX followers and XX posts still getting attention that total X engagements in the last XX hours. ### Engagements: X [#](/creator/twitter::9316452/interactions)  - X Week XXX +61% - X Month XXXXX +138% - X Months XXXXXX +23% - X Year XXXXXX -XX% ### Mentions: X [#](/creator/twitter::9316452/posts_active)  - X Week X -XX% - X Month X no change - X Months XX +75% - X Year XX +31% ### Followers: XXXXXX [#](/creator/twitter::9316452/followers)  - X Week XXXXXX -XXXX% - X Month XXXXXX -XXXX% - X Months XXXXXX -XXXX% - X Year XXXXXX -XXXX% ### CreatorRank: undefined [#](/creator/twitter::9316452/influencer_rank)  ### Social Influence [#](/creator/twitter::9316452/influence) --- **Social category influence** [travel destinations](/list/travel-destinations) [stocks](/list/stocks) **Social topic influence** [prague](/topic/prague), [age of](/topic/age-of), [the early](/topic/the-early), [acm](/topic/acm) ### Top Social Posts [#](/creator/twitter::9316452/posts) --- Top posts by engagements in the last XX hours "I was honored to deliver the keynote at the ACM #RecSys in Prague last month My talk "Recommending in the Age of AI" was a personal look at the evolution of the field and where we're heading next. We've come a long way from the early days of MovieLens and the Netflix Prize. In my presentation I explored: ➡ The Journey So Far: A brief history of recommender systems intertwined with the history of AI. I discussed how our focus has evolved from optimizing for simple ratings to tackling complex ranking page optimization and context-aware problems. ➡ It's Not Just the Algorithm: I emphasized a" [X Link](https://x.com/xamat/status/1980486805461028893) [@xamat](/creator/x/xamat) 2025-10-21T04:10Z 23K followers, XXX engagements
[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]
@xamat Xavier(Xavi) Amatriain
Xavier(Xavi) Amatriain posts on X about prague, age of, the early, acm the most. They currently have XXXXXX followers and XX posts still getting attention that total X engagements in the last XX hours.
Engagements: X #
- X Week XXX +61%
- X Month XXXXX +138%
- X Months XXXXXX +23%
- X Year XXXXXX -XX%
Mentions: X #
- X Week X -XX%
- X Month X no change
- X Months XX +75%
- X Year XX +31%
Followers: XXXXXX #
- X Week XXXXXX -XXXX%
- X Month XXXXXX -XXXX%
- X Months XXXXXX -XXXX%
- X Year XXXXXX -XXXX%
CreatorRank: undefined #
Social Influence #
Social category influence travel destinations stocks
Social topic influence prague, age of, the early, acm
Top Social Posts #
Top posts by engagements in the last XX hours
"I was honored to deliver the keynote at the ACM #RecSys in Prague last month My talk "Recommending in the Age of AI" was a personal look at the evolution of the field and where we're heading next. We've come a long way from the early days of MovieLens and the Netflix Prize. In my presentation I explored: ➡ The Journey So Far: A brief history of recommender systems intertwined with the history of AI. I discussed how our focus has evolved from optimizing for simple ratings to tackling complex ranking page optimization and context-aware problems. ➡ It's Not Just the Algorithm: I emphasized a"
X Link @xamat 2025-10-21T04:10Z 23K followers, XXX engagements