The Power of Suggestion: An Evaluation of the Effects of Source and Position on the Use of Query Suggestions

This study examines whether the perceived source of query suggestions and their location within the search interface affects users' likelihood of clicking on the suggested queries. Participants were asked to complete three search tasks using the search system provided. The query suggestions were placed on either left or right side of the page, and were labeled as either system-generated or as having come from other users of the system. Participants also evaluated their engagement with the search tasks and the quality and usefulness of the query suggestions. Results indicated that users presented with query suggestions on the left scored significantly higher on two measures of engagement. While the effects of source did not meet tests of statistical significance, participants who believed the query suggestions came from other users had higher mean scores for three of the four search engagement scales and the query suggestion rating scale

A location-query-browse graph for contextual recommendation

Traditionally, recommender systems modelled the physical and cyber contextual influence on people's moving, querying, and browsing behaviours in isolation. Yet, searching, querying and moving behaviours are intricately linked, especially indoors. Here, we introduce a tripartite location-query-browse graph (LQB) for nuanced contextual recommendations. The LQB graph consists of three kinds of nodes: locations, queries and Web domains. Directed connections only between heterogeneous nodes represent the contextual influences, while connections of homogeneous nodes are inferred from the contextual influences of the other nodes. This tripartite LQB graph is more reliable than any monopartite or bipartite graph in contextual location, query and Web content recommendations. We validate this LQB graph in an indoor retail scenario with extensive dataset of three logs collected from over 120,000 anonymized, opt-in users over a 1-year period in a large inner-city mall in Sydney, Australia. We characterize the contextual influences that correspond to the arcs in the LQB graph, and evaluate the usefulness of the LQB graph for location, query, and Web content recommendations. The experimental results show that the LQB graph successfully captures the contextual influence and significantly outperforms the state of the art in these applications