We know what you want to buy:a demographic-based system for product recommendation on microblogs

Product recommender systems are often deployed by e-commerce websites to improve user experience and increase sales. However, recommendation is limited by the product information hosted in those e-commerce sites and is only triggered when users are performing e-commerce activities. In this paper, we develop a novel product recommender system called METIS, a MErchanT Intelligence recommender System, which detects users' purchase intents from their microblogs in near real-time and makes product recommendation based on matching the users' demographic information extracted from their public profiles with product demographics learned from microblogs and online reviews. METIS distinguishes itself from traditional product recommender systems in the following aspects: 1) METIS was developed based on a microblogging service platform. As such, it is not limited by the information available in any specific e-commerce website. In addition, METIS is able to track users' purchase intents in near real-time and make recommendations accordingly. 2) In METIS, product recommendation is framed as a learning to rank problem. Users' characteristics extracted from their public profiles in microblogs and products' demographics learned from both online product reviews and microblogs are fed into learning to rank algorithms for product recommendation. We have evaluated our system in a large dataset crawled from Sina Weibo. The experimental results have verified the feasibility and effectiveness of our system. We have also made a demo version of our system publicly available and have implemented a live system which allows registered users to receive recommendations in real time. © 2014 ACM.

Learning user and product distributed representations using a sequence model for sentiment analysis

In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets.

Utilising online shopping environment attributes holistically to create competitive advantage

As more consumers shop online, it becomes crucial for marketers to know how online shopping environments (OSEs) can be used to gain competitive advantage. This dissertation aims to explain theoretically how OSE attributes work together holistically to produce desirable consumer responses, applying and extending a theory from the environmental psychology literature to the online context. Firstly, the study conceptualises OSEs as virtual environments which may be perceived and experienced both cognitively and affectively through a technology-mediated interaction with a computer screen. A multi-disciplinary approach identifies key characteristics of OSEs: they involve consumers; they are more complex than their offline counterparts; they are likely first apprehended holistically; and they can elicit high levels of emotions and cognition. Secondly, the research uses a gestalt approach and extends Kaplan and Kalan’s (1982) Preference Framework, taking account of the specific characteristics of OSEs, which one visits specifically to obtain product information. The results support the proposition that OSEs are perceived in terms of their Sense-making and Exploratory attributes. Thirdly, the research explains how OSE attributes work together to produce desirable consumer responses. As hypothesised, Exploratory potential produces both Hedonic and Utilitarian value, and both kinds of value contribute to Site commitment. An unexpected result is that Sense-making potential does not produce Utilitarian value directly, but only through the mediation of Exploratory potential. The research contributes to marketing theory by: (1) identifying ways the internet has changed the nature of the shopping experience; (2) extending Kaplan and Kaplan’s Preference Framework to explain how consumers perceive OSEs holistically; (3) identifying the distinction between page-level and site-level perceptions, and (4) distinguishing between different sources of information (marketer vs. non-marketer). Managerially, the research provides a model for marketers to conceive and design retail websites whose attributes work together to create competitive advantage.

Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines.

Leveraging product adopter information from online reviews for product recommendation

The availability of the sheer volume of online product reviews makes it possible to derive implicit demographic information of product adopters from review documents. This paper proposes a novel approach to the extraction of product adopter mentions from online reviews. The extracted product adopters are the ncategorise into a number of different demographic user groups. The aggregated demographic information of many product adopters can be used to characterize both products and users, which can be incorporated into a recommendation method using weighted regularised matrix factorisation. Our experimental results on over 15 million reviews crawled from JINGDONG, the largest B2C e-commerce website in China, show the feasibility and effectiveness of our proposed frame work for product recommendation.

Connecting social media to e-commerce:cold-start product recommendation using microblogging information

In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.

Mining product adopter information from online reviews for improving product recommendation

We present in this article an automated framework that extracts product adopter information from online reviews and incorporates the extracted information into feature-based matrix factorization formore effective product recommendation. In specific, we propose a bootstrapping approach for the extraction of product adopters from review text and categorize them into a number of different demographic categories. The aggregated demographic information of many product adopters can be used to characterize both products and users in the form of distributions over different demographic categories. We further propose a graphbased method to iteratively update user- and product-related distributions more reliably in a heterogeneous user-product graph and incorporate them as features into the matrix factorization approach for product recommendation. Our experimental results on a large dataset crawled from JINGDONG, the largest B2C e-commerce website in China, show that our proposed framework outperforms a number of competitive baselines for product recommendation.

The effects of external linkages on new product innovativeness:an examination of moderating and mediating influences

Our study of 116 new product development projects in Taiwanese Information Technology (IT) firms show that horizontal linkages more strongly impact on new product innovativeness than vertical linkages. The firm's learning ability or absorptive capacity increases new product innovativeness. It also moderates the impacts of corporate and research institute linkages on new product innovativeness. Moreover, we confirm that knowledge gains mediate the positive impacts of absorptive capacity and external linkages on new product innovativeness.

Market-level information and the diffusion of competing technologies:an exploratory analysis of the LAN industry

Market-level information diffused by print media may contribute to the legitimation of an emerging technology and thus influence the diffusion of competing technological standards. After analyzing more than 10,000 trade media abstracts from the Local Area Networks (LAN) industry published between 1981 and 2000, we found the presence of differential effects on the adoption of competing standards by two market-level information types: technology and product availability. The significance of these effects depends on the technology's order of entry and suggests that high-tech product managers should make strategic use of market-level information by appropriately focusing the content of their communications. © 2007 Elsevier B.V. All rights reserved.

Process orientation and information technology:customer engagement at Xerox (UK) Ltd

Xerox Customer Engagement activity is informed by the "Go To Market" strategy, and "Intelligent Coverage" sales philosophy. The realisation of this philosophy necessitates a sophisticated level of Market Understanding, and the effective integration of the direct channels of Customer Engagement. Sophisticated Market Understanding requires the mapping and coding of the entire UK market at the DMU (Decision Making Unit) level, which in turn enables the creation of tailored coverage prescriptions. Effective Channel Integration is made possible by the organisation of Customer Engagement work according to a single, process defined structure: the Selling Process. Organising by process facilitates the discipline of Task Substitution, which leads logically to creation of Hybrid Selling models. Productive Customer Engagement requires Selling Process specialisation by industry sector, customer segment and product group. The research shows that Xerox's Market Database (MDB) plays a central role in delivering the Go To Market strategic aims. It is a tool for knowledge based selling, enables productive SFA (Sales Force Automation) and, in sum, is critical to the efficient and effective deployment of Customer Engagement resources. Intelligent Coverage is not possible without the MDB. Analysis of the case evidence has resulted in the definition of 60 idiographic statements. These statements are about how Xerox organise and manage three direct channels of Customer Engagement: Face to Face, Telebusiness and Ebusiness. Xerox is shown to employ a process-oriented, IT-enabled, holistic approach to Customer Engagement productivity. The significance of the research is that it represents a detailed (perhaps unequalled) level of rich description of the interplay between IT and a holistic, process-oriented management philosophy.

Marketing strategy and new product development in the UK credit card industry

The development of new products in today's marketing environment is generally accepted as a requirement for the continual growth and prosperity of organisations. The literature is consequently rich with information on the development of various aspects of good products. In the case of service industries, it can be argued that new service product development is of as least equal importance as it is to organisations that produce tangible goods products. Unlike the new goods product literature, the literature on service marketing practices, and in particular, new service product development, is relatively sparse. The main purpose of this thesis is to examine a number of aspects of new service product development practice with respect to financial services and specifically, credit card financial services. The empirical investigation utilises both a case study and a survey approach, to examine aspects of new service product development industry practice relating specifically to gaps and deficiencies in the literature with respect to the financial service industry. The findings of the empirical work are subsequently examined in the context in which they provide guidance and support for a new normative new service product development model. The study examines the UK credit card financial service product sector as an industry case study and perspective. The findings of the field work reveal that the new service product development process is still evolving, and that in the case of credit card financial services can be seen as a well-structured and well-documented process. New product development can also be seen as an incremental, complex, interactive and continuous process which has been applied in a variety of ways. A number of inferences are subsequently presented.

Developing a model for consumer product safety evaluation

Safety enforcement practitioners within Europe and marketers, designers or manufacturers of consumer products need to determine compliance with the legal test of "reasonable safety" for consumer goods, to reduce the "risks" of injury to the minimum. To enable freedom of movement of products, a method for safety appraisal is required for use as an "expert" system of hazard analysis by non-experts in safety testing of consumer goods for implementation consistently throughout Europe. Safety testing approaches and the concept of risk assessment and hazard analysis are reviewed in developing a model for appraising consumer product safety which seeks to integrate the human factors contribution of risk assessment, hazard perception, and information processing. The model develops a system of hazard identification, hazard analysis and risk assessment which can be applied to a wide range of consumer products through use of a series of systematic checklists and matrices and applies alternative numerical and graphical methods for calculating a final product safety risk assessment score. It is then applied in its pilot form by selected "volunteer" Trading Standards Departments to a sample of consumer products. A series of questionnaires is used to select participating Trading Standards Departments, to explore the contribution of potential subjective influences, to establish views regarding the usability and reliability of the model and any preferences for the risk assessment scoring system used. The outcome of the two stage hazard analysis and risk assessment process is considered to determine consistency in results of hazard analysis, final decisions regarding the safety of the sample product and to determine any correlation in the decisions made using the model and alternative scoring methods of risk assessment. The research also identifies a number of opportunities for future work, and indicates a number of areas where further work has already begun.

The impact of drug information on the prescribing of drugs

Using prescription analyses and questionnaires, the way drug information was used by general medical practitioners during the drug adoption process was studied. Three new drugs were considered; an innovation and two 'me-too' products. The innovation was accepted by general practitioners via a contagion process, information passing among doctors. The 'me-too' preparations were accepted more slowly and by a process which did not include the contagion effect. 'Industrial' information such as direct mail was used more at the 'awareness' stage of the adoption process while 'professional' sources of information such as articles in medical journals were used more to evaluate a new product. It was shown that 'industrial' information was preferred by older single practice doctors who did not specialise, had a first degree only and who did not dispense their own prescriptions. Doctors were divided into early and late-prescribers by using the date they first prescribed the innovatory drug. Their approach to drug information sources was further studied and it was shown that the early-prescriber issued slightly more prescriptions per month, had a larger list size, read fewer journals and generally rated industrial sources of information more highly than late-prescribers. The prescribing habits of three consultant rheumatologists were analysed and compared with those of the general practitioners in the community which they served. Very little association was noted and the influence of the consultant on the prescribing habits of general practitioners was concluded to be low. The consultants influence was suggested to be of two components, active and passive; the active component being the most influential. Journal advertising and advertisement placement were studied for one of the 'me-too' drugs. It was concluded that advertisement placement should be based on the reading patterns of general practitioners and not on ad-hoc data gathered by representatives as was the present practice. A model was proposed relating the 'time to prescribe' a new drug to the variables suggested throughout this work. Four of these variables were shown to be significant. These were, the list size, the medical age of the prescriber, the number of new preparations prescribed in a given time and the number of partners in the practice.

A study of new product market search and user adoption

The research was carried out in the Aviation Division of Dunlop Limited and was initiated as a search for more diverse uses for carbon/carbon composites. An assumed communication model of adoption was refined by introducing the concept of a two way search after making cross industry comparisons of supplier and consumer behaviour. This research has examined methods of searching for new uses for advanced technology materials. Two broad approaches were adopted. First, a case history approach investigated materials that had been in a similar oosition to carbon/carbon to see how other material producing firms had tackled the problem. Second, a questionnaire survey among industrialists examined: the role and identity of material decision makers in different sized firms; the effectiveness of various information sources and channels; and the material adoption habits of different industries. The effectiveness of selected information channels was further studied by monitoring the response to publicity given to carbon/carbon. A flow chart has been developed from the results of this research which should help any material producing firm that is contemplating the introduction of a new material to the world market. Further benefit to our understanding of the innovation and adoption of new materials would accrue from work in the followino areas: "micro" type case histories; understanding more fully the role of product champions or promoters; investigating the phase difference between incremental and radical type innovations for materials; examining the relationship between the adoption rate of new materials and the advance of technology; studying the development of cost per unit function methods for material selection; and reviewing the benefits that economy of scale studies can have on material developments. These are all suggested areas for further work.

Changes in information processing in children and their implications for marketing strategy

This thesis examines children's consumer choice behaviour using an information processing perspective, with the fundamental goal of applying academic research to practical marketing and commercial problems. Proceeding a preface, which describes the academic and commercial terms of reference within which this interdisciplinary study is couched, the thesis comprises four discernible parts. Initially, the rationale inherent in adopting an information processing perspective is justified and the diverse array of topics which have bearing on children's consumer processing and behaviour are aggregated. The second part uses this perspective as a springboard to appraise the little explored role of memory, and especially memory structure, as a central cognitive component in children's consumer choice processing. The main research theme explores the ease with which 10 and 11 year olds retrieve contemporary consumer information from subjectively defined memory organisations. Adopting a sort-recall paradigm, hierarchical retrieval processing is stimulated and it is contended that when two items, known to be stored proximally in the memory organisation are not recalled adjacently, this discrepancy is indicative of retrieval processing ease. Results illustrate the marked influence of task conditions and orientation of memory structure on retrieval; these conclusions are accounted for in terms of input and integration failure. The third section develops the foregoing interpellations in the marketing context. A straightforward methodology for structuring marketing situations is postulated, a basis for segmenting children's markets using processing characteristics is adopted, and criteria for communicating brand support information to children are discussed. A taxonomy of market-induced processing conditions is developed. Finally, a case study with topical commercial significance is described. The development, launch and marketing of a new product in the confectionery market is outlined, the aetiology of its subsequent demise identified and expounded, and prescriptive guidelines are put forward to help avert future repetition of marketing misjudgements.