Studies find that at least 20% of web queries have local intent; and the fraction of queries with local intent that originate from mobile properties may be twice as high. The emergence of standardized support for location providers in web browsers, as well as of providers of accurate locations, enables so-called hyper-local web querying where the location of a user is accurate at a much finer granularity than with IP-based positioning. This paper addresses the problem of determining the importance of points of interest, or places, in local-search results. In doing so, the paper proposes techniques that exploit logged directions queries. A query that asks for directions from a location a to a location b is taken to suggest that a user is interested in traveling to b and thus is a vote that location b is interesting. Such user-generated directions queries are particularly interesting because they are numerous and contain precise locations. Specifically, the paper proposes a framework that takes a user location and a collection of near-by places as arguments, producing a ranking of the places. The framework enables a range of aspects of directions queries to be exploited for the ranking of places, including the frequency with which places have been referred to in directions queries. Next, the paper proposes an algorithm and accompanying data structures capable of ranking places in response to hyper-local web queries. Finally, an empirical study with very large directions query logs offers insight into the potential of directions queries for the ranking of places and suggests that the proposed algorithm is suitable for use in real web search engines.
Venetis et al. REF use map queries to determine the importance of places of interest; their work is more on the efficient indexing of direction queries and a ranking methodology based on it.
Hyper-Local, Directions-Based Ranking of Places
PVLDB
PVLDB
Computer Science