sichunluo/LAMAR

[ICASSP'24] "Large Language Models Augmented Rating Prediction in Recommender System"

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Experimental

This project helps businesses improve how accurately they recommend products or content to their customers. By combining detailed descriptions of items with typical user preferences, it produces more precise predictions of what a user will like. This is useful for anyone managing a recommendation system, like e-commerce managers, content strategists, or platform operators.

No commits in the last 6 months.

Use this if you want to enhance your existing recommendation engine's accuracy by incorporating rich textual information about items and user preferences.

Not ideal if you don't have access to descriptive text for your items or if your primary need is a recommendation system from scratch rather than augmenting an existing one.

e-commerce content-recommendation customer-engagement personalization marketing

No License Stale 6m No Package No Dependents

Maintenance 0 / 25

Adoption 4 / 25

Maturity 8 / 25

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