Hyper-local, directions-based ranking of places

Abstract

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.

Spatio-semantic user profiles in location-based social networks

“…For example, users' personal characteristics such as age, gender, and cuisine preferences were used in [21], and social affinity was considered in [22,23]. User's history of online activity can also be collected, for example, search history; history of map browsing and spatial searching logs [24][25][26], place reviews and ratings [27][28][29], as well as explicit interaction on LBSN, by tagging and commenting on places [30,31]. In this work, users' location tracks are considered as the primary source of user-place relationships, as these represent explicit interaction with geographic places, normally recording actual visits to places.…”

Mobile devices and APIs in the framing of Hyperlocal Journalism

“…Ainda assim, apesar do alcance mundial das redes, mesmo quando com um simples clique pode-se obter informações a partir de qualquer lugar do mundo, os estudos revelam que pelo menos 20% das consultas da Web tem a intenção e obter informação local (VENETIS et al 2011). Quando se utiliza um serviço móvel, esta fração de consultas com a intenção local pode ser duas vezes maior.…”

LARS: A Location-Aware Recommender System

“…Conversely, LARS is the first location-based service to consider implicit preferences by using location-based ratings to help users discover new and interesting items. Recent research has proposed the problem of hyper-local place ranking [25]. Given a user location and query string (e.g., "French restaurant"), hyper-local ranking provides a list of top-k points of interest influenced by previously logged directional queries (e.g., map direction searches from point A to point B).…”