Secure Identification in Social Wireless Networks

The applications based on social networking have brought revolution towards social life and are continuously gaining popularity among the Internet users. Due to the advanced computational resources offered by the innovative hardware and nominal subscriber charges of network operators, most of the online social networks are transforming into the mobile domain by offering exciting applications and games exclusively designed for users on the go. Moreover, the mobile devices are considered more personal as compared to their desktop rivals, so there is a tendency among the mobile users to store sensitive data like contacts, passwords, bank account details, updated calendar entries with key dates and personal notes on their devices. The Project Social Wireless Network Secure Identification (SWIN) is carried out at Swedish Institute of Computer Science (SICS) to explore the practicality of providing the secure mobile social networking portal with advanced security features to tackle potential security threats by extending the existing methods with more innovative security technologies. In addition to the extensive background study and the determination of marketable use-cases with their corresponding security requirements, this thesis proposes a secure identification design to satisfy the security dimensions for both online and offline peers. We have implemented an initial prototype using PHP Socket and OpenSSL library to simulate the secure identification procedure based on the proposed design. The design is in compliance with 3GPP‟s Generic Authentication Architecture (GAA) and our implementation has demonstrated the flexibility of the solution to be applied independently for the applications requiring secure identification. Finally, the thesis provides strong foundation for the advanced implementation on mobile platform in future

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Digitizing Offline Shopping Behavior Towards Mobile Marketing

The proliferation of mobile technologies makes it possible for mobile advertisers to go beyond the real-time snapshot of the static location and contextual information about consumers. In this study, we propose a novel mobile advertising strategy that leverages full information on consumers’ offline moving trajectories. To evaluate the effectiveness of this strategy, we design a large-scale randomized field experiment in a large shopping mall in Asia based on 83,370 unique user responses for two weeks in 2014. We found the new mobile trajectory-based advertising is significantly more effective for focal advertising store compared to several existing baselines. It is especially effective in attracting high-income consumers. Interestingly, it becomes less effective during the weekend. This indicates closely targeted mobile ads may constrict consumer focus and significantly reduce the impulsive purchase behavior. Our finding suggests marketers should carefully design mobile advertising strategy, depending on different business contexts

Multi-Dimensional-Personalization in mobile contexts

During the dot com era the word "personalisation” was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate. Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services – with the consequence that the right information or service could therefore be offered at the right place, at the right time. The growing availability of "Location-based Services“ for mobile phones is a new target for the use of personalisation. "Location-based Services“ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range / short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place. This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the user’s privacy so that an approach supporting anonymous recommendations through an extended "Chinese Wall“ will be described

Dataretrieving for varied in different Composition Databases using Content aggregation

Keeping in mind with a variety of content choices, consumers are exhibiting diverse preferences for content; their preferences often depend on the context in which they consume content as well as various exogenous events. To satisfy the consumers� demand for such diverse content, multimedia content aggregators (CAs) haveemerged which gather content from numerous multimedia sources. A key challenge for such systems is to accurately predict whattype of content each of its consumers prefers in a certain context,and adapt these predictions to the evolving consumers preferences, contexts, and content characteristics This paper addressesgenerate text based file data sets, such as word, text files, image file data sets, and video file data sets, It also extract data from multiple databases, evaluate user preference based query, reduce time complexity by clustering data, and increase fetching speed by using query classification

Real-Time Purchase Prediction Using Retail Video Analytics

The proliferation of video data in retail marketing brings opportunities for researchers to study customer behavior using rich video information. Our study demonstrates how to understand customer behavior of multiple dimensions using video analytics on a scalable basis. We obtained a unique video footage data collected from in-store cameras, resulting in approximately 20,000 customers involved and over 6,000 payments recorded. We extracted features on the demographics, appearance, emotion, and contextual dimensions of customer behavior from the video with state-of-the-art computer vision techniques and proposed a novel framework using machine learning and deep learning models to predict consumer purchase decision. Results showed that our framework makes accurate predictions which indicate the importance of incorporating emotional response into prediction. Our findings reveal multi-dimensional drivers of purchase decision and provide an implementable video analytics tool for marketers. It shows possibility of involving personalized recommendations that would potentially integrate our framework into omnichannel landscape

Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning

Digital human recommendation system has been developed to help customers find their favorite products and is playing an active role in various recommendation contexts. How to timely catch and learn the dynamics of the preferences of the customers, while meeting their exact requirements, becomes crucial in the digital human recommendation domain. We design a novel practical digital human interactive recommendation agent framework based on Reinforcement Learning(RL) to improve the efficiency of the interactive recommendation decision-making by leveraging both the digital human features and the superior flexibility of RL. Our proposed framework learns through real-time interactions between the digital human and customers dynamically through the state-of-art RL algorithms, combined with multimodal embedding and graph embedding, to improve the accuracy of personalization and thus enable the digital human agent to timely catch the attention of the customer. Experiments on real business data demonstrate that our framework can provide better personalized customer engagement and better customer experiences.Comment: 9 pages, 1 figure, 1 table, the paper has been accepted and this is the final camera-ready for NeurIPS 2022 Workshop on Human in the Loop Learning, https://neurips-hill.github.io

Barriers and paradoxical recommendation behaviour in online to offline (O2O) services. A convergent mixed-method study

Mobile apps offering online to offline (O2O) services act as aggregators providing interface for delivery of required products and services at a preferred location. Despite offering multiple affordances, many O2O services have not diffused as anticipated, indicating the existence of consumer resistance towards them. One such example is that of food delivery apps (FDAs), which are experiencing resistance at both the pre-adoption and post-adoption stage. However, there are scarce empirical findings explicating the pre-and post-adoption barriers perceived to be associated with FDAs. The present study addresses this gap by utilising the Innovation Resistance Theory (IRT) and a convergent mixed-method study design to examine the barriers that existing FDA users face and how these impinge on their trust and valence of recommendation behaviour (positive and negative word of mouth). The study not only extends the classic IRT barriers to the FDA-context by identifying three key barriers (economic, efficiency, and experience) but also offers empirical evidence to support the negative association of barriers with trust and paradoxical recommendation behaviour by analysing data collected from 303 FDA users through Prolific. The findings also support the mediation effect of trust and the moderation effect of advertisement overload on the identified associations, making interesting theoretical and practical contributions. publishedVersio