Steam Recommender using similarity! (Undergraduate Student Project) [P]
Our take
Introducing the enhanced Steam Recommender—a project designed to transform how you discover new games. This system goes beyond traditional tags, using unique gameplay vectors to highlight what makes each game special. For example, it captures specific elements like day cycles and themes, making your search more personalized and enjoyable. By focusing on what you love about your favorite games, the Recommender helps you find hidden gems while avoiding the pitfalls of repetitive suggestions.
The recent development of a Steam recommender system using similarity metrics reveals a significant stride in how we engage with video game recommendations. This project, presented by an undergraduate student, stands out for its focus on providing users with not just game suggestions but also the reasoning behind those recommendations. In an era where users are inundated with options, such personalized insights can enhance decision-making and enrich the gaming experience. This approach aligns with the broader trend in data management and recommendation systems, as seen in recent innovations like TabPFN-3 just released: a pre-trained tabular foundation model for up to 1M rows, which emphasizes the importance of tailored solutions in data-heavy environments.
At the heart of this recommender is a thoughtful examination of what makes games appealing on a granular level. The student creator reflects on personal gaming preferences, dissecting elements like gameplay focus and thematic tags to create a more nuanced recommendation framework. This contrasts sharply with traditional systems that rely heavily on broad categorizations, such as "action" or "adventure." By breaking down games into specific vectors—like the social simulation aspects of a game or its unique art style—this model not only enhances the user’s exploration but also encourages them to think critically about their preferences. This aligns with the insights shared in articles like 5 Useful Python Scripts for Time Series Analysis, which explore the power of breaking down complex data into more manageable components for clearer analysis.
The implications of such systems extend beyond mere user satisfaction; they signal a shift towards a more human-centered approach in technology. As the market becomes saturated with similar games, the need for innovative tools that can help users discover hidden gems becomes increasingly crucial. The recommender's ability to introduce underrated games helps to address a prevalent issue in gaming: the tendency for popular titles to overshadow smaller, yet equally engaging, alternatives. This democratization of game discovery not only benefits players but also empowers developers who create unique experiences that may not fit within mainstream categories.
Moreover, the student's willingness to invite feedback and acknowledge the challenges faced during development illustrates a crucial aspect of modern tech culture: collaboration and iteration. As they navigate the complexities of building a robust database, they exemplify the spirit of innovation that drives technology forward. This aspect of their project resonates with the themes discussed in Presentation: Beyond Coding: How Senior ICs Grow Influence and Drive Impact, where the importance of adaptability and continuous learning in tech careers is highlighted.
Looking ahead, the evolution of recommendation systems like this one will likely shape not only the gaming industry but also other sectors reliant on personalized user experiences. As AI continues to develop and integrate into various applications, understanding the nuances of user preferences will become increasingly vital. Will we see more projects emerge that focus on demystifying the recommendation process, prioritizing user education and empowerment? The potential for transformation in how we interact with technology is vast, and the gaming community stands at the forefront of this exciting journey.
![Steam Recommender using similarity! (Undergraduate Student Project) [P]](/_next/image?url=https%3A%2F%2Fpreview.redd.it%2Fcdmk3yirrq0h1.png%3Fwidth%3D140%26height%3D140%26crop%3D1%3A1%2Csmart%26auto%3Dwebp%26s%3D2976c3fb0b0ed8d1f18cd7ead1fbd673682bfdb8&w=3840&q=75)
![Steam Recommender using similarity! (Undergraduate Student Project) [P]](https://preview.redd.it/cdmk3yirrq0h1.png?width=140&height=140&crop=1:1,smart&auto=webp&s=2976c3fb0b0ed8d1f18cd7ead1fbd673682bfdb8 "Steam Recommender using similarity! (Undergraduate Student Project) [P]") | (DISCLAIMER: I accidentally deleted the last post on this subreddit my apologies if this is your second time seeing it) Last year I made a post about my steam recommender The last one was great and served its purpose of showing many people new games, But this new version is much more functional! I love making recommendation systems that tell the user WHY they got the recommendation. During a steam sale event, I always find myself trying to look for new video games to play. If I wanted to find a new game I would try to whittle it down by using steam tags, but the steam tag system is very broad "action". could apply to many many games. That got me thinking, what aspects do I like about my favorite games? Well I like Persona 4 because of the city vibes and jazz fusion, Spore because of the unique character creation and whimsical theme. Balatro for its unique deck building synergies. What if I could capture unique tags that identify a game that aren't just "action" and put them into vectors to show the (focus) of a game For example I could break persona 4 into something like Game play Focus vector: Tags: I find that this system makes searching for games more "fun" now I can see why I like balatro. I like it because of the card synergies not so much for its rogue-like nature. I also find that this helps find new underrated games, and beats the trap that Collaborative Filtering algorithms that get into where it "feels" like you get recommended the same things. find your next favorite game! : https://nextsteamgame.com/ pull a PR!: https://github.com/BakedSoups/NextSteamGame ( I actually made some git issues myself for problems I can't fix) if anyone has any criticism I would love to hear it! this is probably my favorite passion project. I made this during final season, Since the database takes around 1 day to build, there were some inevitable rate limiting errors that I go into. So I am sure there are many bugs. if you come across any and are willing to share that would be Amazing. Hope this website helps people find new games! Also I have a advance mode for people that don't mind messing with sliders and weird data terms. [link] [comments] |
Read on the original site
Open the publisher's page for the full experience
Related Articles
- Steam Recommender using similarity! pt 2 (Student Project)I Just made a sequel to my Steam Game recommender website! Last year I made a post about my steam reccomender The last one was great and served its purpose of showing many people new games, But this new version is much more functional! I love making recommendation systems that tell the user WHY they got the recommendation. During a steam sale event, I always find myself trying to look for new video games to play. If I wanted to find a new game I would try to whittle it down by using steam tags, but the steam tag system is very broad "action". could apply to many many games. That got me thinking, what aspects do I like about my favorite games? Well I like Persona 4 because of the city vibes and jazz fusion, I like Spore because of the unique character creation and whimsical theme. and I like Balatro for its unique deck building synergies. What if I could capture unique tags that identify a game that aren't just "action" and put them into vectors to show the (focus) of a game For example I could break persona 4 into something like Gameplay Focus vector: - Day cycle 20% - Dungeon crawling 20% - Social sim 20% Tags: - Music: jazz fusion - Vibe: Small rural town I achieved this by pulling 2k reviews for 80k steam games, running them through a 4 stage pipeline that filters out the reviews to find reviews describing a video game's vibes or structure, then asking a llm to generate these reviews into vectors, niche anchor tags and micro tags using non canonical names. all to "capture" niche tags that could never be found normally. Then I used a 6 stage pipeline to group these non canonical names together (fast combat = speedy action combat) From that I stored it all in PostgreSQL + Chroma db, made an app using React. and Shipped it all within a docker container inside a digital ocean droplet! The result is a cool little steam game recommender that I can use to not just find similar games, but find games that share my favorite aspect of a game I like. A system that explains to me why I got the recommendations I got. I find that this system makes searching for games more "fun" now I can see why I like balatro. I like it because of the card synergies not so much for its rogue-like nature. I also find that this helps find new underrated games, and beats the trap that Collaborative Filtering algorithms that get into where it "feels" like you get recommended the same things. find your next favorite game! : https://nextsteamgame.com/ pull a PR!: https://github.com/BakedSoups/NextSteamGame ( I actually made some git issues myself for problems I can't fix) if anyone has any criticism I would love to hear it! this is probably my favorite passion project. Hope this website helps people find new games! Also I have a advance mode for people that don't mind messing with sliders and weird data terms. submitted by /u/Expensive-Ad8916 [link] [comments]
- Steam Recommend pt 2 (Student Project)I Just made a sequel to my Steam Game recommender website! Last year I made a post about my steam reccomender The last one was great but this one I'm glad I was able to make a product that hopefully helped people find their next game. After some developing I made a new one that is much more functional! I love making recommendation systems that tell the user WHY they got the recommendation. During a steam sale event, I always find myself trying to look for new video games to play. If I wanted to find a new game I would try to whittle it down by using steam tags, but the steam tag system is very broad "action". could apply to many many games. That got me thinking, what aspects do I like about my favorite games? Well I like Persona 4 because of the city vibes and jazz fusion, I like Spore because of the unique character creation and whimsical theme. and I like Balatro for its unique deck building synergies. What if I could capture unique tags that identify a game that aren't just "action" and put them into vectors to show the (focus) of a game For example I could break persona 4 into something like Gameplay Focus vector: - Day cycle 20% - Dungeon crawling 20% - Social sim 20% Tags: - Music: jazz fusion - Vibe: Small rural town I achieved this by pulling 2k reviews for 80k steam games, running them through a 4 stage pipeline that filters out the reviews to find reviews describing a video game's vibes or structure, then asking chatgpt to generate these reviews into vectors, niche anchor tags and micro tags using non canonical names. Then I used a 6 stage pipeline to group these non canonical names together (fast combat = speedy action combat) From that I stored it all in PostgreSQL + Chroma db, made an app using React. and Shipped it all within a docker container inside a digital ocean droplet! The result is a cool little steam game recommender that I can use to not just find similar games, but find games that share my favorite aspect of a game I like. A system that explains to me why I got the recommendations I got. I find that this system makes searching for games more "fun" now I can see why I like balatro. I like it because of the card synergies not so much for its rogue-like nature. I also find that this helps find new underrated games, and beats the trap that Collaborative Filtering algorithms that get into where it "feels" like you get recommended the same things. find your next favorite game! : https://nextsteamgame.com/ pull a PR!: https://github.com/BakedSoups/NextSteamGame ( I actually made some git issues myself for problems I can't fix) if anyone has any criticism I would love to hear it! this is probably my favorite passion project. Hope this website helps people find new games! Also I have a advance mode for people that don't mind messing with sliders and weird data terms. submitted by /u/Expensive-Ad8916 [link] [comments]
- Steam Similarity Recommender [P]I Just made a sequel to my Steam Game recommender website! Last year I made a post about my steam recommender The last one was great but this one I'm glad I was able to make a product that hopefully helped people find their next game. After some developing I made a new one that is much more functional! I love making recommendation systems that tell the user WHY they got the recommendation. During a steam sale event, I always find myself trying to look for new video games to play. If I wanted to find a new game I would try to whittle it down by using steam tags, but the steam tag system is very broad "action". could apply to many many games. That got me thinking, what aspects do I like about my favorite games? Well I like Persona 4 because of the city vibes and jazz fusion, I like Spore because of the unique character creation and whimsical theme. and I like Balatro for its unique deck building synergies. What if I could capture unique tags that identify a game that aren't just "action" and put them into vectors to show the (focus) of a game For example I could break persona 4 into something like Game play Focus vector: - Day cycle 20% - Dungeon crawling 20% - Social sim 20% Tags: - Music: jazz fusion - Vibe: Small rural town I achieved this by pulling 2k reviews for 80k steam games, running them through a 4 stage pipeline that filters out the reviews to find reviews describing a video game's vibes or structure, then asking chatgpt to generate these reviews into vectors, niche anchor tags and micro tags using non canonical names. Then I used a 6 stage pipeline to group these non canonical names together (fast combat = speedy action combat) From that I stored it all in PostgreSQL + Chroma db, made an app using React. and Shipped it all within a docker container inside a digital ocean droplet! The result is a cool little steam game recommender that I can use to not just find similar games, but find games that share my favorite aspect of a game I like. A system that explains to me why I got the recommendations I got. I find that this system makes searching for games more "fun" now I can see why I like balatro. I like it because of the card synergies not so much for its rogue-like nature. I also find that this helps find new underrated games, and beats the trap that Collaborative Filtering algorithms that get into where it "feels" like you get recommended the same things. find your next favorite game! : https://nextsteamgame.com/ pull a PR!: https://github.com/BakedSoups/NextSteamGame ( I actually made some git issues myself for problems I can't fix) if anyone has any criticism I would love to hear it! this is probably my favorite passion project. Hope this website helps people find new games! Also I have a advance mode for people that don't mind messing with sliders and weird data terms. submitted by /u/Expensive-Ad8916 [link] [comments]
- Steam Similarity Recommender Find your next favorite game and learn WHY (student project)[P]I love making recommendation systems that tell the user WHY they got the recommendation. During a steam sale event, I always find myself trying to look for new video games to play. If I wanted to find a new game I would try to whittle it down by using steam tags, but the steam tag system is very broad "action". could apply to many many games. That got me thinking, what aspects do I like about my favorite games? Well I like Persona 4 because of the city vibes and jazz fusion, I like Spore because of the unique character creation and whimsical theme. and I like Balatro for its unique deck building synergies. What if I could capture unique tags that identify a game that aren't just "action" and put them into vectors to show the (focus) of a game For example I could break persona 4 into something like Gameplay Focus vector: - Day cycle 20% - Dungeon crawling 20% - Social sim 20% Tags: - Music: jazz fusion - Vibe: Small rural town I achieved this by pulling 2k reviews for 80k steam games, running them through a 4 stage pipeline that filters out the reviews to find reviews describing a video game's vibes or structure, then asking a llm to generate these reviews into vectors, niche anchor tags and micro tags using non canonical names. to really "capture" niche tags that can't be captured normally. Then I used a 6 stage pipeline to group these non canonical names together (fast combat = speedy action combat) From that I stored it all in PostgreSQL + Chroma db, made an app using React. and Shipped it all within a docker container inside a digital ocean droplet! The result is a cool little steam game recommender that I can use to not just find similar games, but find games that share my favorite aspect of a game I like. A system that explains to me why I got the recommendations I got. I find that this system makes searching for games more "fun" now I can see why I like balatro. I like it because of the card synergies not so much for its rogue-like nature. I also find that this helps find new underrated games, and beats the trap that Collaborative Filtering algorithms that get into where it "feels" like you get recommended the same things. find your next favorite game! : https://nextsteamgame.com/ pull a PR!: https://github.com/BakedSoups/NextSteamGame ( I actually made some git issues myself for problems I can't fix) if anyone has any criticism I would love to hear it! this is probably my favorite passion project. Hope this website helps people find new games! Also I have a advance mode for people that don't mind messing with sliders and weird data terms. submitted by /u/Expensive-Ad8916 [link] [comments]