Exploring Real-Time Temporal Query Auto-Completion

Query auto-completion (QAC) is a common interactive feature for assisting users during query formulation. Following each query input keystroke, QAC suggests queries prefixed by the input characters; allowing the user to avoid further cognitive and physical effort if any are acceptable. To rank suggestions, QAC approaches typically aggregate past query popularity to determine the likelihood of a query being used again. Hence, QAC is usually very effective for consistently popular queries. However, as the web becomes increasingly real-time, more people are turning to search engines to find out about unpredictable emerging and ongoing events and phenomena. QAC approaches reliant on aggregating long-term historic query-logs are not sensitive to very recent real-time events, because newly popular queries will be outweighed by long-term popular queries, especially for less-specific prefix lengths (e.g. 2 or 3 characters). We explore limiting the aggregation period of past querylog evidence to increase the temporal sensitivity of QAC. We vary the query-log aggregation period between 2 and 14 days, for prefix lengths of 2 to 5 characters. Experimentation simulates a realtime environment using openly available MSN and AOL query-log datasets. Analysis indicates a linear relationship between prefix length and QAC performance when using different query-log aggregation periods. In particular, we find QAC for shorter prefix lengths is optimal when a shorter query-log aggregation period is used, and vice-versa, longer prefix lengths benefit from a longer query-log aggregation period.