The paper introduces ATARS, a GPT-4-based pipeline that extracts atypical item aspects, scores their utility for a user, and re-ranks recommendations, correlating with manual serendipity rankings.
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Engineering Serendipity through Recommendations of Items with Atypical Aspects
The paper introduces ATARS, a GPT-4-based pipeline that extracts atypical item aspects, scores their utility for a user, and re-ranks recommendations, correlating with manual serendipity rankings.