This paper aims to explore a mixed-initiative approach to find analogies in the context of product ideas to help people innovate. This system focuses on a corpus of product innovation from Quirky.com and aims to explore the feasibility of learning simple, structural representations of the problem schemas to find out analogies. The purpose and mechanism of each product idea are extracted and a vector representation is constructed. This is used to find analogies in order to inspire people to generate creative ideas. Three experiments were conducted as part of this study. The first experiment involved AMT crowd workers annotating the product ideas to segregate the purpose and mechanism. These annotations were used to construct the vectors in order to compare and compute the analogies. As part of the second experiment, 8000 Quirky products were chosen and crowd workers were asked to use the search interface to find out analogies for 200 seed documents. Finally, a within-study experiment was conducted wherein the participants were asked to redesign an existing product. For the given product idea, the participants received 12 product inspirations retrieved using the system developed, 12 using TF-IDF, and 12 were retrieved randomly. The system developed aimed to retrieve near-purpose, far-mechanism analogies to help users come up with innovative ideas. The results showed that the ideas generated by using the system’s results were more creative than the other two conditions.
Analogies often prove to be a source of inspiration and/or competitive analysis. In the case of inspiration, cross-domain analogies are specifically extremely helpful and can be difficult to find. Also, given the rate at which information generation is growing, it has become all the more difficult to explore and find analogies. Such systems would definitely help save time by predicting potential analogies from a large dataset. I feel that the system would definitely help come up with creative, alternate solutions when compared to traditional information retrieval systems.
With respect to the ideation evaluation experiment, the results showed that the randomly generated analogies actually were more successful in helping users come up with creative ideas when compared to the TF-IDF condition. This shows that the traditional information retrieval technologies would not work well as is in the setting of analogy predictions and needs to be tweaked in order to help serve the purpose of inspiring users. I feel that there is potential to expand the core concepts of the proposed system and use it in different applications. For instance, finding similar content could be augmented into a recommendation system where the system could recommend content similar to the user’s browsing history.
- What are your thoughts about the system proposed? Do you think this system is scalable?
- The study uses only purpose and mechanism to compare and predict analogies. Do you think these parameters are sufficient? Are there any other features that can be extracted to improve the system?
- The paper mentions the need for extensions to generalize the solution to apply the system to other domains. Apart from the suggestions in the paper, what are some potential extensions?
I like the idea you suggest about using this system to find similar content for a recommendation system based on the user’s browsing history. I think it would be interesting to see such a study being performed.
With respect to your second question, I believe that additional parameters could be needed, but these are dependent on the domain/task under consideration. For example, the paper on ‘SOLVENT: A Mixed Initiative System for Finding Analogies between Research Papers’ added ‘background’ and ‘findings’ in addition to purpose and mechanism. I believe it would be tricky to determine a one size fits all solution.