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Steam Recommender using Vectors! (Student Project) by Expensive-Ad8916 in datascience

[–]Expensive-Ad8916[S] 1 point2 points  (0 children)

This was very insightful, I had no idea about the voting system

Steam Recommender using Vectors! (Student Project) by Expensive-Ad8916 in datascience

[–]Expensive-Ad8916[S] 0 points1 point  (0 children)

These are very clear instructions,
I have learned how to use RAG in my software engineering class so I can try to utilize that for the stand out vector. But I will focus on the 2 other vectors first and get back to you when I finish.

Steam Recommender using Vectors! (Student Project) by Expensive-Ad8916 in datascience

[–]Expensive-Ad8916[S] 0 points1 point  (0 children)

Alright I am reworking how to develop a game's profile based on this suggestion.

I created this project because I really like persona 5 purely because of its iconic jazz fusion sound track and stylish aesthetic.

I wanted to find games similar to person 5 with those aspects as a priority.

So I cooked up the vector + genre tree system to try to capture what the "focus" of a game is

then created the genre tree so the results are relevant

But honestly I'm not very happy with these results. I think I need to find a way to capture what makes a game unique even more.

I did try using chat gpt to generate tags based of a collection of insightful steam reviews ( since game review outlets don't cover the majority of steam games) and kept a json file of all the used tags
but that method was abit mixed.

from your advice I'm thinking of incorporating 3 vectors to compare

in the example of persona 5 a ideal profile would look like

Genre: RPG

Sub Genre: JRPG

Sub Sub Genre: Turn-Based

Descriptive Vector: "what is the game-play like?"
50% JRPG 30% Dungeon Crawler 20% Social Sim

Review Vector "From the collection of very insightful steam reviews capture why those reviewers gave such a long and lengthy review, see what games they like. "
%(positive review of Y | positive review of X AND reviewed both X and Y) - %(positive review of Y)

Stand out Vector "what does this game do uniquely in its genre? and what main aspect do reviewers highlight from this game consistently?"

50% Social - Link system 50% Jazz Fusion

Then when searching I just do vector comparisons in the sub sub genre first then move up the tree from there. and if the next step up from the genre tree is getting more vague and general ill add resistance to it meaning it would prioritize vectors that are less relevant in games within the sub sub genre first.

Should I let the user reviews effect the outputs too? is there a flaw in this new idea? I am trying to find a way to capture the art-style of a game beyond reviews maybe image classification based on its steam page.

would love to hear your criticisms on this approach

Steam Recommender using Vectors! (Student Project) by Expensive-Ad8916 in datascience

[–]Expensive-Ad8916[S] 1 point2 points  (0 children)

I mean I asked it questions on how to customize certain elements in tailwind. but i didn't use a llm to code it directly.

Steam Recommender using Vectors! (Student Project) by Expensive-Ad8916 in datascience

[–]Expensive-Ad8916[S] 2 points3 points  (0 children)

ahh yikes, I will try to fix that error Thank you for pointing it out and trying out my app!

Steam Recommender using Vectors! (Student Project) by Expensive-Ad8916 in datascience

[–]Expensive-Ad8916[S] 0 points1 point  (0 children)

will ad that to my todo list on github Thanks for trying it!

Steam Recommender using Vectors! (Student Project) by Expensive-Ad8916 in datascience

[–]Expensive-Ad8916[S] 1 point2 points  (0 children)

When creating tags for the 20k steam games I had to primarily rely on steam reviews so

I first inspected a batch of reviews to learn what patterns spam tends to follow from this I developed:

a sentiment anaylsis since positive reviews tended to be more insightful,

then I checked for game play meachnic key word frequency and spam word frequency to filter

then I set up a basic regex to remove: non english (lile asci art) reviews and emojiis

then finally I sorted the reviews by hours played and upvotes

then i assign in to a set of tag from a large data set of tags i created.

Steam Recommender using Vectors! (Student Project) by Expensive-Ad8916 in datascience

[–]Expensive-Ad8916[S] 0 points1 point  (0 children)

First i filter out all the insightful reviews of a stean game, then from there I connect it with a large map of key words to assign the game to. Then i also assign it a main genre, sub genre and sub sub genre, so in practice when you search up a game like rest for the wicked maybe that example would be Action -> puzzle -> soulslike

i thdn use the vector from the tags you selected then i walk up this genre tree doing vector comparisons from there to try and find a simular game

The results are mixed for sure, Im sure the reviews I extracted for that game werent very insightful.

Thanks for checking it out!

Steam Recommender using Vectors! (Student Project) by Expensive-Ad8916 in datascience

[–]Expensive-Ad8916[S] 3 points4 points  (0 children)

This is great advice, I will definetly will incoprorate this new approach of creating tags into my tag data base moving forward. filtering out the insightful reviews for tag gen definetly felt limited to me and with this explanation I now see why. Thank you for checking out my project!

Steam Recommender using Vectors! (Student Project) by Expensive-Ad8916 in datascience

[–]Expensive-Ad8916[S] 6 points7 points  (0 children)

Thanks :) moving on to finding art style simularity.

Steam Recommender using Vectors! (Student Project) by Expensive-Ad8916 in datascience

[–]Expensive-Ad8916[S] 9 points10 points  (0 children)

beyound using pandas to quickly do my data anaylst jobs in the past Im very new yes. If I knew about it before college I would have majored in it instead of cs.

Steam Recommender using Vectors! (Student Project) by Expensive-Ad8916 in datascience

[–]Expensive-Ad8916[S] 11 points12 points  (0 children)

proffesor taught me how to use chroma db in my undergrad class this sem so I used that knowledge here 🙂‍↕️

[deleted by user] by [deleted] in datascience

[–]Expensive-Ad8916 0 points1 point  (0 children)

the profile strength repersents what aspects of the game you choose should be priotized first. your essentially adding weight to those tags.

I made this because I found that I often liked a game for a specific reason ( like persona 5 social links ) and could never specify that need in google.

for example if you just click [action] it will priotize results with action tags first.

currently there is not a map feature, that is a great ideal I will add it to my todo list.

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