Answering recreational web searches with relevant things to do results. Issue 2 (March 2020) - Alonso, Omar; Kandylas, Vasileios; Tremblay, Serge-Eric; Whiting, Stewart
Highlights: We propose the problem of recreational queries in information retrieval and propose a solution that combines search query logs with LBSNs. We describe a taxonomy for recreational queries derived from real world data by examining a real-world query log (Bing.com) We introduce a relevance model that incorporates spatial, temporal information, and social data. We detail the offline evaluation of the POIs proposed by our techniques. This topic is usually not covered nor described in detailed in related work. We showed that assessing POIs is very complex and propose design alternatives that work well. To summarize, we present an end-to-end data driven system that uses LBSN data to solve a common web search scenario. Abstract: Recreational queries from users searching for places to go and things to do or see are very common in web and mobile search. Users specify constraints for what they are looking for, like suitability for kids, romantic ambiance or budget. Queries like "restaurants in New York City" are currently served by static local results or the thumbnail carousel. More complex queries like "things to do in San Francisco with kids" or "romantic places to eat in Seattle" require the user to click on every element of the search engine result page to read articles from Yelp, TripAdvisor, or WikiTravel to satisfy their needs. Location data, which is an essential part of web search, is even more prevalent with location-based social networks and offers new opportunities for many ways of satisfying information seeking scenarios. In this paper, we address the problem of recreational queries in information retrieval and propose a solution that combines search query logs with LBSNs data to match user needs and possible options. At the core of our solution is a framework that combines social, geographical, and temporal information for a relevance model centered around the use of semantic annotations on Points of Interest with the goal of addressing these recreational queries. A central part of the framework is a taxonomy derived from behavioral data that drives the modeling and user experience. We also describe in detail the complexity of assessing and evaluating Point of Interest data, a topic that is usually not covered in related work, and propose task design alternatives that work well. We demonstrate the feasibility and scalability of our methods using a data set of 1B check-ins and a large sample of queries from the real-world. Finally, we describe the integration of our techniques in a commercial search engine.