We evaluate the possibility of using within-item measures of meta-data similarity to improve recommendation rankings along psychologically salient dimensions of incongruity and creativity. This approach contrasts with recently developed methods at introducing diversity into recommendations which rely on across-item measurements of dissimilarity, while sharing several formal and algorithmic elements. We show that semantic distance based operationalizations of psychological constructs show substantial correlation with empirical data. We further show that incongruity predicts variability in satisfaction as measured by movie ratings in a large corpus. The results from a two month-long user study demonstrate that incongruity-based recommendations attract considerably more interaction from users, and users expressed significantly greater satisfaction given these recommendations. Based on these observations, we proposed that using incongruity to diversify recommendations may be useful in expanding recommendation repertoires along interesting psychological dimensions, complementing relevance-based search.
This is a joint work with Prof. Nisheeth Srivastava
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Our Paper has been accepted in
RecSys, Rio De Janeiro (Brazil), 2020