Using latent features diversification to reduce choice difficulty in recommendation lists

\u3cp\u3eAil important side effect of using recoinmender systems is a phenomenon called choice overload ; the negative feeling incurred by the increased difficulty to choose from large sets of high quality recommendations. Choice overload has traditionally been related to the size of the item set, but recent work suggests that the diversity of the item set is an important moderator. Using the latent feanires of a matrix factorization algorithm, we were able to manipulate the diversity of the items, while controlling the overall attractiveness of the list of recommendations. In a user study, participants evaluated personalized item lists (varying in level of diversity) on perceived diversity and attractiveness, and their experienced choice difficulty and tradeoff difficulty. The results suggest that diversifying the recommendations might be an effective way to reduce choice overload, as perceived diversity and attractiveness increase with item set diversity, subsequently resulting in participants experiencing less tradeoff difficulty and choice difficulty.\u3c/p\u3