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As a popular technique in recommender systems, Collaborative Filtering (CF) has received extensive attention in recent years. However, its privacy-related issues, especially for neighborhood-based CF methods, can not be overlooked. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Private Neighbor Collaborative Filtering (PNCF) algorithm. The algorithm includes two privacy-preserving operations: Private Neighbor Selection and Recommendation-Aware Sensitivity. Private Neighbor Selection is constructed on the basis of the notion of differential privacy to privately choose neighbors. Recommendation-Aware Sensitivity is introduced to enhance the performance of recommendations. Theoretical and experimental analysis are provided to show the proposed algorithm can preserve differential privacy while retaining the accuracy of recommendations.

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We analyze 18-million rows of Wi-Fi access logs collected over a 1-year period from over 120,000 anonymized users at an inner city shopping mall. The anonymized data set gathered from an opt-in system provides users' approximate physical location as well as web browsing and some search history. Such data provide a unique opportunity to analyze the interaction between people's behavior in physical retail spaces and their web behavior, serving as a proxy to their information needs. We found that (a) there is a weekly periodicity in users' visits to the mall; (b) people tend to visit similar mall locations and web content during their repeated visits to the mall; (c) around 60% of registered Wi-Fi users actively browse the web, and around 10% of them use Wi-Fi for accessing web search engines; (d) people are likely to spend a relatively constant amount of time browsing the web while the duration of their visit may vary; (e) the physical spatial context has a small, but significant, influence on the web content that indoor users browse; and (f) accompanying users tend to access resources from the same web domains.

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.

Understanding the association between customer demographics and behaviour is critical for operators of indoor retail spaces. This study explores such an association based on a combined understanding of customer Cyber (online), Physical, and (some aspects of ) Social (CPS) behaviour, at the conjunction of corresponding CPS spaces. We combine the results of a traditional questionnaire with large-scale WiFi access logs, which capture customer cyber and physical behaviour. We investigate the predictability of user demographics based on CPS behaviors captured from both sources. We find (1) strong correlations between users' demographics and their CPS behaviors; (2) log-recorded cyber-physical behavior reflects well data captured in the corresponding questionnaire; (3) different CPS behaviors contribute differently to the predictability of demographic attributes; and (4) the predictability of user demographics from logs is comparable to questionnaire-based data. As such, our study provides strong support for demographic studies based on large-scale logs data capture.