When selecting ideas or trying to find inspiration, designers often must sift through hundreds or thousands of ideas. This paper provides an algorithm to rank design ideas such that the ranked list simultaneously maximizes the quality and diversity of recommended designs. To do so, we first define and compare two diversity measures using Determinantal Point Processes (DPP) and additive sub-modular functions. We show that DPPs are more suitable for items expressed as text and that a greedy algorithm diversifies rankings with both theoretical guarantees and empirical performance on what is otherwise an NP-Hard problem. To produce such rankings, this paper contributes a novel way to extend quality and diversity metrics from sets to permutations of ranked lists. These rank metrics open up the use of multi-objective optimization to describe trade-offs between diversity and quality in ranked lists. We use such trade-off fronts to help designers select rankings using indifference curves. However, we also show that rankings on trade-off front share a number of top-ranked items; this means reviewing items (for a given depth like the top 10) from across the entire diversity-to-quality front incurs only a marginal increase in the number of designs considered. While the proposed techniques are general purpose enough to be used across domains, we demonstrate concrete performance on selecting items in an online design community (OpenIDEO), where our approach reduces the time required to review diverse, high-quality ideas from around 25 hours to 90 minutes. This makes evaluation of crowd-generated ideas tractable for a single designer. Our code is publicly accessible for further research.
The main research questions within both recommender systems and information retrieval are two-fold: (1) how do we represent this diminishing marginal utility, and once we do (2) how do we optimize over it efficiently? For the former question, researchers have proposed alternate scoring methods to diversify rankings. An early exemplar of this was Ziegler al @cite_14 who modeled the topics in text documents and then tried to balance the topics within recommended lists. Their large scale user survey showed that a user's overall satisfaction with lists depended on both accuracy and the perceived diversity of list items. Approaches that followed largely centered around the notion of that a diverse set should somehow cover a space of items well. The main differentiators of past approaches are how this coverage is measured and then combined with other objectives such as document relevance.
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In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.Our work builds upon prior research on recommender systems, looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evaluate our method using book recommendation data, including offline analysis on 361, !, 349 ratings and an online study involving more than 2, !, 100 subjects.