It has been recently claimed that human movement is highly predictable. While an upper bound of 93% predictability was shown, this was based upon human movement trajectories of very high spatiotemporal granularity. Recent studies reduced this spatiotemporal granularity down to the level of GPS data, and under a similar methodology results once again suggested a high predictability upper bound (i.e. 90% when movement was quantized down to a spatial resolution approximately the size of a large building). In this work we reconsider the derivation of the upper bound to movement predictability. By considering real-world topological constraints we are able to achieve a tighter upper bound, representing a more refined limit to the predictability of human movement. Our results show that this upper bound is between 11-24% less than previously claimed at a spatial resolution of approx. 100mx 100m, with a greater im provement for finer spatial resolutions. This indicates that human mobility is potentially less predictable than previously thought.We provide an in-depth examination of how varying the spatial and temporal quantization affects predictability, and consider the impact of corresponding limits using a large set of real-world GPS traces. Particularly at fine-grained spatial quantizations, where a significant number of practical applications lie, these new (lower) upper limits raise serious questions about the use of location information alone for prediction, contributing more evidence that such prediction must integrate external variables.

No abstract

The interactions of users with search engines can be seen as implicit relevance feedback by the user on the results offered to them. In particular, the selection of results by users can be interpreted as a confirmation of the relevance of those results, and used to reorder or prioritize subsequent search results. This collection of search/result pairings is called clickthrough data, and many uses for it have been proposed. However, the reliability of clickthrough data has been challenged and it has been suggested that clickthrough data are not a completely accurate measure of relevance between search term and results. This paper reports on an experiment evaluating the reliability of clickthrough data as a measure of the mutual relevance of search term and result. The experiment comprised a user study involving over 67 participants and determines the reliability of image search clickthrough data, using factors identified in previous similar studies. A major difference in this work to previous work is that the source of clickthrough data comes from image searches, rather than the traditional text page searches. Image search clickthrough data were rarely examined in prior works but has differences that impact the accuracy of clickthrough data. These differences include a more complete representation of the results in image search, allowing users to scrutinize the results more closely before selecting them, as well as presenting the results in a less obviously ordered way. The experiment reported here demonstrates that image clickthrough data can be more reliable as a relevance feedback measure than has been the case with traditional text‐based search. There is also evidence that the precision of the search system influences the accuracy of click data when users make searches in an information‐seeking capacity.

Service research suggests homes are becoming increasingly connected as consumers automate and personalize new forms of service provision. Yet, large-scale empirical evidence on how and why consumers automate smart domestic products (SDPs) is lacking. To address this knowledge gap, we analyze 13,905 consumer-crafted, automated combinations of SDPs, totaling 1,144,094 installations, across 253 separate service providers on the web service IFTTT.com . An exploratory network analysis examines the topology of the network and an interpretive coding exercise reveals how consumers craft different styles of human-computer interaction to cocreate value. The results reveal that the SDP network is disassortative, is imbalanced, has a long-tailed degree distribution, and that popular services have high centrality across all product category combinations. We show that popular combinations of SDPs are primarily motivated by utilitarian value-seeking enacted through a preference for automated tasks outside of conscious attention, though more individualistic combinations are slightly more likely to be hedonistically inclined. We conclude by showing how these consumer-crafted forms of service provision within domestic environments reveal design redundancy and opportunities for service innovation.