Mining Mobile User Preferences for Personalized Context-Aware Recommendation

In this paper, we illustrate how to extract personal context-aware preferences from the context-rich device logs (i.e., context logs) for building novel personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his/her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context independent and context dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world data set show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.

Typical tag recommendation systems for photos shared on social networks such as Flickr, use visual content analysis, collaborative filtering or personalization strategies to produce annotations. However, the dependence on manual intervention and the knowledge of sufficient personal preferences coupled with the folksonomic issues limit the scope of these strategies. In this paper, we present a fully automatic and folksonomically scalable tag recommendation model that can recommend tags for a user's photos without an explicit knowledge of the user's personal tagging preferences. The model is learned using the collective tagging behavior of other users in the user's local interaction network, which we believe approximates the user's preferences, at least partially. The tag recommendation model generates content-based annotations and then uses a Naive Bayes formulation to translate these annotations to a set of folksonomic tags selected from the tags used by the users in the local interaction network. Quantitative and qualitative comparisons with 890 Flickr networks show that this approach is highly useful for tag recommendation in the presence of insufficient information of a user's own preferences.

This paper proposes a semantic image adaptation scheme for heterogeneous mobile display devices. This scheme aims to provide mobile users with the most desired image content by integrating the content semantic importance with user preferences under limited mobile display constraints. The main contributions of the proposed scheme are: 1) seamless integration of mobile user supplied query information with low level image features to identify semantically important image contents; 2) integration of semantic importance and user feedback to dynamically update mobile user preferences; and 3) perceptually optimized adaptation for image display on mobile devices. In order to bridge the semantic gap for adaptation, we design a Bayesian fusion approach to properly integrate low level features with high level semantics. To accommodate the variation of user preferences, the system involves mobile users in the adaptation process with only a few simple feedbacks so as to present to the users most interesting content on mobile devices. Eventually, perceptually optimized adaptation is performed to present the best image content for mobile users according to mobile display capacities. Extensive experiments have been carried out based on several common events defined in Kodak's consumer image database. These experiments show that by utilizing the proposed semantic adaptation scheme with integration of the semantics and mobile user preferences, perceptually relevant adaptation can be effectively carried out to tailor the image towards user intentions under the mobile environment constraints.

Nowadays, mobile users are constantly being connected and increasingly asked to express their personal preferences in the digital world. User preferences deal with simple device settings options, like notification alarms, as well as relevant ethical choices relating to the user behavior, privacy ones included (e.g., concerning the unauthorized disclosure and mining of personal data, as well as the access to restricted resources). All these preferences define the user, they are the building blocks of her digital identity and will be increasingly important given the growing rise of autonomous technologies and their ethical implications. The settings that enable these preferences are often hard to locate and hard to understand, even in popular apps and operating systems. Moreover, they can expose privacy, be employed for profiling or exploited for malicious activities. In this landscape, we devise the introduction of a Personal Preferences Automation Module (PPAM) capable of automatically inferring, applying and enforcing user choices in multiple scenarios ranging from speeding up simple time consuming tasks, to managing sensitive ethical choices. The wide range of sensors and devices that can be found in the mobile domain makes it a privileged context in which to employ the proposed module. In this paper, we present two application scenarios and describe the proposed approach at work on them.

The aim of personalizing web information systems is to tailor content (media) to the user's personal preferences, goals and context, in turn increasing the reusability of that content. However, most developers are increasingly seeking to apply 'Web as a Platform' based approaches where web-based content is integrated with web services to provide the platform and environment features that were traditionally offered by desktop environments and applications. Therefore, next generation personalized web information systems need to be capable of dynamically personalizing, adapting and integrating web content and web services to create customized experiences for individual users. The goal of the research presented in this paper is to provide innovative techniques and technologies to achieve this. As part of this research, a novel architecture for the generation of Personalized Learning Activities, which highlights both the need for, and the power of combining and personalizing content and services has been developed. The system has been evaluated in the context of a Higher Education use case based on the user's perception of three key elements of the Personalized Learning Activity: the personalization, the usability and the educational benefits.

In this paper we present a Japanese-English transfer- based machine translation system. Our main research contribution is that the transfer rules are not handcrafted but are learnt automatically from a parallel corpus. We learn specific transfer rules from sentence pairs, which are then generalized in a consolidation phase to avoid overtraining. The system has been implemented in Amzi! Prolog, which offers scalability for large rule bases, full Unicode support for Japanese characters, and several APIs for the seamless integration of the translation functionality into common office environments, e.g. Microsoft Word. The dynamic nature of our system allows for an easy customization of the rule base according to the user's personal preferences.

In many of today's software systems, most notably online social networks, users can share personal information. Behind the simple action of sharing is a more complicated thought process regarding privacy: which content to share, with whom to share, and why to share. For a user, it's time-consuming and error-prone to check individual personal content for privacy violations. Hence, it would be ideal if a personal assistant can learn its users' privacy preferences and subsequently help users' decision-making by signaling potentially private content. A personalized privacy assistant can help its user make privacy decisions taking into account the ambiguity and uncertainty of privacy predictions as well as its user's personal preferences. Moreover, an explanation of why an image is considered public or private can aid the user in understanding the assistant's decisions.

In order to identify a user's personal preference in navigating the Web or to recommend collected Web information, it is very useful to analyze the user's Web-browsing behavior. However, it is difficult to determine which Web-browsing behaviors are influential on predicting a user's interest because each individual has his/her own habit and personal manner in surfing the Web and locating documents of interest. In this study, we propose an adaptive personalized recommender system based on a preference-thesaurus constructed by learning a user's Web-browsing behavior. The major components of the proposed recommender system are the Web-browsing behavior monitor, preference-thesaurus constructor, relevant document recommender, and user feedback learner. The adaptive nature of the proposed system allows the personalization of recommendation and the identification of the ever-changing influential Web-browsing behaviors. A proof-of-concept system is implemented and experiments are performed to verify the system's capability to personalize recommendation and to learn through user's feedbacks.

What makes someone attractive has been examined for faces, in terms of common preferences, and for opposite-sex interactions. We expanded on this by considering also other non-verbal modalities, personal preferences and same-sex interactions. We presented the face, body motion, voice and body odour from 61 non-verbal agents (34 women) as stimuli in an attractiveness rating to 71 perceivers (37 women). Our results showed that the modalities were differently attractive and that some correlated more than others. Specifically, body odours were least and audio-video stimuli most attractive. Voice/looks as well as body odour/movement showed fairly robust positive associations. Both common and personal preferences accounted for variance in the data. Most effects compared between opposite- and same-sex ratings, with only a few exceptions, including that only same-sex ratings showed a clear dominance of personal over common preferences. We conclude that the different non-verbal modalities are equally relevant for attraction but differ in absolute attractiveness and redundancy, likely due to their different suitability for communicating stable (e.g. genetic) versus variable (e.g. hormonal) person characteristics. Beauty excites agreement and disagreement; it matters not only in encounters with the other sex but in social interactions more broadly.

In this paper, we discuss how a short-range wireless communication scheme called Mobile Encounter Network (MEN) can provide additional value for both the consumer and the service/product provider (the company). When used as an information search system on the market environment, a MEN-based system allows services and products being promoted at the location they are available. From the customer's point of view, MEN may provide a digitally augmented vision, an enhanced view to the current environment. Combined with a set of filtering and search rules this may provide a self-manageable context combining the user's own personal environment and preferences to the services and features available in the current surroundings. An imaginary case study of this kind of system in execution is provided. The conclusion is that such method may can generate additional revenue to the company and please the customers' buying process.

To belter serve users' information needs without requiring comprehensive queries from users, a simple yet effective technique is to explore the preferences of users. Since these preferences can differ for each context of the user, we introduce context-aware preferences. To anchor the semantics of context-aware preferences in a traditional probabilistic model of information retrieval, we present a semantics for context-aware preferences based on the history of the user. An advantage of this approach is that the inherent uncertainty of context information, due to the fact that context information is often acquired through sensors, can be easily integrated in the model. To demonstrate the feasibility of our approach and current bottlenecks we provide a naive implementation of our technique based on database views.

In this paper, we introduce MyCampus, a Semantic Web environment for context-aware mobile services, which we are in the process of developing and validating on Carnegie Mellon University's campus. The environment revolves around a growing collection of customizable agents capable of (semi-) automatically discovering and accessing user personal resources (e.g. calendar, location tracking functionality, food preferences) and Web services as they assist their users in carrying out different tasks such as planning an evening out, organizing a study group or filtering incoming messages. The openness of the MyCampus architecture directly derives from a set of ontologies for describing personal resources, contextual attributes, user preferences and web services. Contextual information and other personal details about a user are accessed via a Semantic e-Wallet, subject to access privileges set by the user.

Shared mobility can fundamentally change road transport and improve life quality. Car-sharing services have reached a high level of growth worldwide and members adopting them as an alternative transportation mode are constantly on the increase. The advent of highly automated vehicles could boost the adoption of car sharing as more inexpensive and convenient on-demand services could be achieved. In recent years, vehicle automation is facilitated and supported by the latest advances in information and communication technologies, forming the cornerstone of the new generation of In-Vehicle Intelligent Systems. The goal of this study is to introduce a novel in-vehicle cognitive framework that utilises (i) driver's/user's profile data and personal preferences, (ii) real-time information associated with the vehicle's driving environment, and (iii) previous knowledge and experience, in proposing adaptations of the vehicle's level of autonomy, in an automated manner, when a driver/user wishes to make a road journey within the automated vehicle sharing context. Knowledge is obtained through the exploitation of Bayesian networking principles. Indicative results on the performance of the proposed cognitive mechanism and the associated knowledge-based selection scheme are presented, mostly with regards to proactively identifying the optimum level of autonomy to be implemented.

A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems

Mobile edge clouds (MEC) is a novel paradigm to augment computation capabilities of mobile devices for resource-scarce applications. This paper first investigates the computation offloading decision making problem among mobile users and leverages the variability in types of tasks, capabilities of mobile devices and user preferences. As it is NP-hard to compute an optimal solution, this paper transforms the problem into a system utility maximization problem. This paper formulates the system utility maximization problem as a resource allocation offloading mechanism (RAOM) for achieving efficient computation offloading. This paper then designs a resource allocation offloading algorithm that can maximize system utility within the constraints of system bandwidth and maximum instructions per second allowed in edge clouds and quantifies its efficiency ratio over the optimal solutions in terms of two vital performance metrics. This paper further extends the study to the scenario of multi-user computation offloading in the wireless interference model. Numerical results demonstrate that the extension is worthwhile and the proposed mechanism RAOM achieves efficient computation offloading performance either with interference or without interference.