Shuo Chang
Peng Dai
Ed H. Chi
ABSTRACT
A relatively new feature in Google Play Store presents mobile app search results grouped by topic, helping users to quickly navigate and explore. The underlying Search Results Clustering (SRC) system faces several challenges, including grouping search results in topical coherent clusters as well as finding the appropriate level of granularity for clustering. We present AppGrouper, an alternative approach to algorithmic-only solutions, incorporating human input in a knowledge-graph-based clustering process. AppGrouper provides an interactive interface that lets domain experts steer the clustering process in early, mid, and late stages. We deployed and evaluated AppGrouper with internal experts. We found that AppGroup improved quality of algorithm-generated app clusters on 56 out of 82 search queries. We also found that the internal experts made more changes in early and mid stages for lower-quality algorithmic results, focusing more on narrow queries. Our result suggests, in some contexts, machine learning systems can greatly benefit from steering from human experts, creating a symbiotic working relationship.
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Index Terms
- AppGrouper: Knowledge-based Interactive Clustering Tool for App Search Results
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