Creating context as you go

Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or context logs for short, and exploit these identified preferences for building 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 or 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 dataset 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.

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.

Purpose– The purpose of this paper is to shed light upon how retailers view alternative payment forms and to what extent they are willing to risk offending their customers by imposing payment restrictions.Design/methodology/approach– This exploratory study consists of three consecutive parts: first, 100 situations of paying for goods or services; second, interviews with 25 of these 100 retailers; and third, observations at a meeting between retailers and bank representatives on various aspects of card and cash payments.Findings– Retailers are unwilling to risk offending their customers and do not normally undertake any actions to affect the customers’ choice of payment form, except for proactively or reactively excluding the use of certain expensive credit cards, and card payments for small amounts. The retailers only take the risk of causing customer dissatisfaction when they feel that the sacrifice for not doing so is too costly, and in these cases the salespersons act very late in the purchase process. Other aspects than payment costs (such as safety, time and environment) seem to have little impact on individual retailers’ actions at the payment stage.Research limitations/implications– The present study focuses solely on the retailers’ point of view on the payment stage, implying a need for additional research on customers’ and bank representatives’ views on the same matter.Practical implications– Retailers try to nurture their customer relationships also when they are proactive or reactive, i.e. by pointing to the high cost of a particular payment form and/or asking customers to help with small change. Sending signals that invite customers to assist may not only be a way to affect how customers pay, but also foster relationship development.Social implications– It seems that environmental costs have not filtered down to the firm level, at least not in an observable way. Any further move towards a “cashless society” has to emanate from other sources.Originality/value– No previous study has focused on the way selling companies approach their customers at the payment stage in terms of proactive, reactive and inactive behaviour.

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.

In service settings, customer satisfaction is often influenced by the quality of the interpersonal interaction between the customer and the contact employee. Previous research has identified the sources of satisfaction and dissatisfaction in service encounters from the customer's point of view; this study explores these sources in service encounters from the contact employee's point of view. Drawing on insights from role, script, and attribution theories, 774 critical service encounters reported by employees of the hotel, restaurant, and airline industries are analyzed and compared with previous research. Results generally support the theoretical predictions and also identify an additional source of customer dissatisfaction—the customer's own misbehavior. The findings have implications for business practice in managing service encounters, employee empowerment and training, and managing customers.

PurposeThe purpose of this paper is to examine the methods used by marketers over the past years to develop loyalty among customers. Furthermore, the paper considers the shortcomings of these methods and certain societal and technological changes that have further impacted on the potential effectiveness of these methods in the future.Design/methodology/approachThe author uses his experience working with hundreds of marketers around the world developing, managing, analyzing, measuring and improving customer loyalty strategies and tactics. The author combines these experiences with analysis of certain dynamics in the marketplace that have changed the way marketers must interact with customers. Finally, the author has combined this experience and analysis to formulate a point of view about the impact on marketing strategies in the future.FindingsMany of the past efforts used by marketers to develop customer loyalty have been ineffective because they have been based on tactics used in the airline industry. These shortcomings will become greater in the future unless marketers change their ways. Corporate scandals have eroded the trust that customers have in marketers and, therefore, marketing efforts must be more genuine and easily understood or customers will react skeptically. Concerns about privacy and identity theft have heightened concerns among customers and made them more wary and, therefore, less willing to openly share information and engage in a relationship with a brand unless there appear to be genuine value and behavior by the company that engender trust and faith.Practical implicationsMarketers must step back and make absolutely certain that their efforts are based on customer needs and desires and do more to enhance customer trust than to erode it. Marketing strategies and programs must be believable. One of the most important components of customer relationships moving forward will be the development of trust that further enhances the customers' willingness to interact with certain brands and companies.Originality/valueThere is a great deal of talk in the marketplace about customer relationships. This paper seeks to bring the dialogue down to earth and incorporate the customer's point of view rather than just the theoretical marketing point of view. This paper suggests that marketers should work harder and work smarter to be sure that their programs are worthwhile to customers.

Understanding music streaming services via text mining of online customer reviews

We present XSearcher, an associative memory based desktop search system, which exploits associations by creating semantic links of personal desktop resources from explicit and implicit user activities. With these links, associations among memory fragments can be built or rebuilt in a user's brain during a search. The personalized ranking scheme uses these links together with a user's personal preferences to rank results by both relevance and importance. XSearcher enhances traditional keyword based search systems since it is closer to the way that human associative memory works.

Virtual-reality-based interactions with virtual agents (VAs) are likely subject to similar influences as human-human interactions. In either real or virtual social interactions, interactants try to maintain their personal space (PS), an ubiquitous, situative, flexible safety zone. Building upon larger PS preferences to humans and VAs with angry facial expressions, we extend the investigations to whole-body emotional expressions. In two immersive settings-HMD and CAVE-66 males were approached by an either happy, angry, or neutral male VA. Subjects preferred a larger PS to the angry VA when being able to stop him at their convenience (Sample task), replicating previous findings, and when being able to actively avoid him (Pass By task). In the latter task, we also observed larger distances in the CAVE than in the HMD.

Recommendation System has become more customized than ever. However, it suffers from cold start and data sparsity issues. Cross Domain Recommendation (CDR) has been proposed as a promising and feasible approach which yields recommendations in the destination domain based on the characteristic and personalized preferences of the users in the source domain. Since the fondness varies for each user, their taste bridge would also be different. Most of the well-known existing models make use of common preference bridge when it comes to transferring preferences for all the users in the system. Personalized Preference Transfer (PPT) for personalizing user preferences using Characteristic Encoder (CE) where user characteristic is mapped to initializing maps is pro-posed for cold start users and from books for warm start users. In this model, a Meta net-work with user attributes is used to attain the bridge preferences for individual users. The Deep Neural Network (DNN) which is a neural network model is used to provide optimized recommendations to the users. The accuracy and efficiency of personalized user preferences transfer is determined based on the comparisons from different models and train/test ratios, making use of two evaluation metrics MAE (Mean Absolute Error) and RMSD (Root Mean Squared Deviation).