Abstract: This disclosure relates to a content cluster system that provides a framework for leveraging large language models (LLMs) to tag content items (e.g., interactive multimedia content items, such as gaming content items) with attribute tags and, based on attribute tags for related content items, generating cluster descriptions of a cluster of content items. Features of the content cluster system involve tagging content items, determining multi-dimensional embeddings for the content items, and clustering the content items based on proximity of values contained within the multi-dimensional embeddings. The content cluster system may further utilize the LLM to generate one or more cluster descriptions based on the associated tags to create a more creative and dynamic representation of related groupings of titles (e.g., gaming titles). By utilizing LLMs and machine learning resources, the content cluster system provides a scalable approach to conventional approaches in determining and presenting groupings of titles.

Abstract: A method of recommending an unplayed game application to a user includes obtaining a game history associated with a user profile, wherein the game history includes at least one game record of a game application the user profile has been used to play, and evaluating the game history to obtain a plurality of game events associated with a game application of the game history. The method further includes accessing a table of tags associated with the plurality of game events associated with the game application, aggregating a plurality of tags in a tag list in the user profile based at least partially on the table of tags and the plurality of game events, and presenting to the user a recommendation of an unplayed game application based at least partially on the plurality of tags.

Abstract: A method of recommending an unplayed game application to a user includes obtaining a game history associated with a user profile, wherein the game history includes at least one game record of a game application the user profile has been used to play, and evaluating the game history to obtain a plurality of game events associated with a game application of the game history. The method further includes accessing a table of tags associated with the plurality of game events associated with the game application, aggregating a plurality of tags in a tag list in the user profile based at least partially on the table of tags and the plurality of game events, and presenting to the user a recommendation of an unplayed game application based at least partially on the plurality of tags.

Abstract: A method of recommending an unplayed game application to a user includes obtaining a game history associated with a user profile, wherein the game history includes at least one game record of a game application the user profile has been used to play, and evaluating the game history to obtain a plurality of game events associated with a game application of the game history. The method further includes accessing a table of tags associated with the plurality of game events associated with the game application, aggregating a plurality of tags in a tag list in the user profile based at least partially on the table of tags and the plurality of game events, and presenting to the user a recommendation of an unplayed game application based at least partially on the plurality of tags.