Name
Abstract | ||
|---|---|---|
User interaction data (e.g., click data) has proven to be a powerful signal for learning-to-rank models in web search. However, such models require observing multiple interactions across many users for the same query-document pair to achieve statistically meaningful gains. Therefore, utilizing user interaction data for improving search over personal, rather than public, content is a challenging problem. First, the documents (e.g., emails or private files) are not shared across users. Second, user search queries are of personal nature (e.g., \"alice's address\") and may not generalize well across users. In this paper, we propose a solution to these challenges, by projecting user queries and documents into a multi-dimensional space of fine-grained and semantically coherent attributes. We then introduce a novel parameterization technique to overcome sparsity in the multi-dimensional attribute space. Attribute parameterization enables effective usage of cross-user interactions for improving personal search quality -- which is a first such published result, to the best of our knowledge. Experiments with a dataset derived from interactions of users of one of the world's largest personal search engines demonstrate the effectiveness of the proposed attribute parameterization technique. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3018661.3018712 | WSDM |
Keywords | Field | DocType |
User interactions, personal search, attribute parameterization | Data mining,Search engine,Parametrization,Information retrieval,Computer science | Conference |
Citations | PageRank | References |
14 | 0.55 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Bendersky | 1 | 986 | 48.69 |
| Xuanhui Wang | 2 | 1394 | 68.85 |
| Donald Metzler | 3 | 3138 | 141.39 |
| Marc A. Najork | 4 | 2538 | 278.16 |