Combining ChatGpt and Criticker

[oplars]

I just discovered a new way to find movies/shows!! A 2-step process involving ChatGpt and Criticker.

It can be difficult to choose an appropriate film to watch when using Criticker's ratings as the only reference. Just because it has a high PSI doesn't mean you're interested in watching a movie of that genre right away.
As a result, I thought of utilizing ChatGpt (I need to get into the routine of always considering ChatGpt as an option when I have a difficulty).
So I asked it how I should use it to find a movie I would really enjoy watching:

It answered:
1. Tell me your favorite movies: Share a list of your favorite movies or describe what you like about them. This will help me understand your taste and suggest similar movies that you might enjoy.

2. Mention your preferred genres: Tell me which movie genres you enjoy the most, such as action, drama, comedy, sci-fi, romance, etc. This will allow me to suggest movies within those genres that have been well-received by audiences and critics.

3. Specify actors, directors, or other film personnel: If you have favorite actors, directors, or other film industry professionals, let me know. I can recommend movies they've been involved in, which might increase the chances you'll like the recommendations.

4. Define specific themes or elements: If you're looking for movies with particular themes or elements (e.g., strong female leads, plot twists, or coming-of-age stories), let me know. I can suggest movies that fit your criteria.

5. Request movies from a specific time period: If you prefer movies from a particular era, such as classic Hollywood or the 1980s, let me know. I can recommend highly regarded movies from that time.

6. Consider your mood: Tell me what kind of movie you're in the mood for (e.g., light-hearted, thought-provoking, or thrilling). I can tailor my recommendations to match your current mood.

Point 1 doesn't require a lot of time. Just go into your favorites and mark all the highest scores and copy/paste everything into ChatGpt. Afterwards, specify the genre and atmosphere you would like it to recommend. Also what you DON’T want e.g. horror, anime, documentary….
You can then evaluate the recommendations in Criticker. And be surprised to see that all of its suggestions have high PSI! Most of the 10 movies it suggests might be movies you already have seen. But then just ask for more and adjust the parameters along the way. I had to tell it that some suggestions were too cynical. That caused it to swing to the opposite direction, suggesting almost family movies. And in this way you can keep correcting if that is necessary.

I discovered the movie for tonight and 10 movies/shows on my watch list that I'm looking forward to watching in no time!

[Bryce74]

I tried ChatGPT too, for recommendations, but it didn't work for me. So I will continue with Criticker which gives much more personalised recommendations and I will continue to compare with IMDB ratings. I had hoped for more with chatgpt but it was quite disappointing. Even after a lot of talking to it, recommendations did not get much better. Even after feeding it my top rated criticker movies and series.

[NIV7]

Okay but you should stop using AI until the Earth is safe

https://www.theguardian.com/technology/ ... -emissions

I only linked one article but c'mon, read some news please.

[Scholar69420]

Sam Altman says that AI may ultimately require up to 10% of global energy demand to power the compute needed. However, if it helps invent new technologies that benefit the planet then it'll be worth it. The cat is out of the bag, nobody is going to stop using AI.

[VinegarBob]

I've been messing around with MetaAI the last few days. I came up with a novel way - pun intended - of getting recommendations from it. During a casual conversation about movies, books and music I told it my top ten favorite novels then asked it to recommend 10 films based on that list. It came up with an interesting batch of films, some of which I hadn't seen, and one of which I just watched today. I thoroughly enjoyed the film.

Chatting with an LLM as if it were a human friend is a fascinating and pretty fruitful way of generating all sorts of recommendations. 10/10 would recommend.

[Scholar69420]

Yup, the idea of AI performing in a social role is very interesting, especially with the personalisation LLMs offer. Instead of telling it my preferences I uploaded the CSV export of my criticker ratings, it even grounds discussion in the context of my mini-reviews.

[fringe]

I don't think that's gonna work.

[amazedemon]

You got me curious, so I shared my csv (feature films only) with an AI and asked it a few questions about average movie ratings per release year. These were the key insights.

Key Insights:
1. Recent years (2012-2022) have significantly more ratings than older years. This could be
due to more active users rating recent movies or a bias towards rating newer films.
2. The average scores for these recent, highly-rated years are lower than what we initially
saw for some older years. This suggests that when we have more data (more ratings), we
get a more balanced view of movie quality.
3. There's considerable variation in scores within each year, as shown by the box plot. This
indicates diverse opinions about movies released in the same year.
4. The year 2012 stands out with the highest average score among these recent years, while
2020 has the lowest. This could be interesting to explore further - perhaps 2012 was
considered a particularly good year for movies, while 2020 (the start of the COVID-19
pandemic) might have affected movie quality or viewer perceptions.

This analysis provides a more reliable picture of the ratings, as it's based on years with a
substantial number of ratings. The initial high scores we saw for years like 1947 were likely
outliers based on very few ratings, which can skew the results.
Would you like to explore any specific aspect of this data further?

I then went further down the rabbit hole trying to get it to make recommendations based on films I hadn't watched, but it faired less well. The recommendations were mostly films I had already seen, but not in my top 10.

I have a pdf with the conversation and python code it generated, but not the charts and tables, if anyone's interested.

[oplars]

It falls short in effectively recommend movies one would give high ratings (its only metric is likely overall ratings from sources like IMDB). But it can present you with movies of the same type as the ones you have already seen, based on the huge amount of data it has available. Therefore, this approach works best in tandem with checking the PSI on Criticker.
Something that is very annoying (and rather strange) is that it keeps offering movies that are already in the cvs-file. Despite repeating multiple times "remember to check if the recommendations include movies already watched".

[christof]

I love this idea, but ChatGPT is not the AI tool for this job. Instead, I downloaded all my reviews as a simple text file and added it to a NotebokLM research topic.
https://notebooklm.google.com/

I typed the following prompt: "What are Christof's film preferences and what does that say about him (both good and bad). Here is the response:

Christof's Film Preferences and What They Say About Him
Christof seems to appreciate a wide range of films, but shows a particular preference for films that are:

Surreal and Thought-Provoking: Many of his highly-rated films, such as "The Holy Mountain" [1], "O Lucky Man!" [2], and "Crimes of the Future" [3], employ surrealism and explore complex philosophical themes. He praises "The Holy Mountain" as the "height of genius" [1] and describes "Crimes of the Future" as "the most Cronenbergiest film ever" [3].
Satirical and Ironic: He enjoys films like "Her" [4], "The Love Witch" [4], "Starship Troopers" [5], and "Propaganda" [6], which use satire and irony to critique aspects of society and culture. He finds "The Love Witch" to be a "cracker of a film" that "has much to say about the often toxic invention of modern love" [4].
Documentaries with Social Commentary: He frequently gives high ratings to documentaries that tackle social and political issues, such as "The Century of the Self" [7], "The Way of All Flesh" [8], "The Trap: What Happened to Our Dream of Freedom" [9], and "Speciesism: The Movie" [10]. He commends "Speciesism: The Movie" for its comprehensive exploration of the concept of "speciesism" and the narrator's intelligent and witty approach [10].
Based on these preferences, one could infer that Christof is:

Positive Traits:

Intellectually curious: His interest in surrealism, complex themes, and social issues suggests a desire to engage with challenging and thought-provoking material.
Critical thinker: His appreciation for satire and social commentary indicates a willingness to question societal norms and examine issues from multiple perspectives.
Open to a variety of genres: His ratings reveal an openness to exploring different styles of filmmaking, ranging from experimental art films to classic musicals.

Potential Negative Traits:

Pretentious: His dismissal of those who don't understand "The Holy Mountain" [1] hints at a possible tendency towards intellectual elitism.
Overly Analytical: His focus on intellectual aspects of films sometimes comes at the expense of emotional engagement, as seen in his reviews of "Solaris" [11, 12] and "Ad Astra" [13].
Cynical: His enjoyment of dark humor and dystopian themes might suggest a somewhat cynical worldview.

Other Observations:

Christof seems to have a particular fondness for the work of director David Cronenberg [3, 14].
He also appreciates films that showcase strong female characters [4, 15, 16].
He often expresses his opinions with strong language and humor, which can be both engaging and off-putting.
Overall, Christof's film ratings suggest a reviewer who is intelligent, discerning, and engaged with film as a medium for exploring ideas and critiquing society. However, his reviews also hint at a possible tendency towards intellectual elitism and cynicism.

Finally...
The autogenerated podcast feature for this is officially hilarious!

What next.
I think to get AI recommendations, it would need a source of new releases to compare against. I am playing with copying a new release list from IMDB like this: https://www.imdb.com/search/title/?titl ... 2024-12-31

(It did predict what film I already have cued up next to watch as a number 1 recommendation and said why i might like it!)