dmhyun/PERIS
Official code of Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability [CIKM'22]
This project helps e-commerce managers and product strategists improve their recommendations to users. By taking a customer's historical purchasing or browsing data, it predicts what items they are most likely to interact with next. The output is a personalized list of recommended items for each user, allowing businesses to offer more relevant suggestions and potentially increase engagement or sales.
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Use this if you manage an online platform and want to provide highly relevant, personalized item recommendations to your users based on their past interactions.
Not ideal if you need a recommendation system that relies solely on item characteristics or user demographics rather than their sequential interaction history.
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Language
Python
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Last pushed
May 04, 2023
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