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.

A snapshot of the lives of women with polycystic ovary syndrome:a photovoice investigation

Polycystic ovary syndrome affects 6  percent of women. Symptoms include hirsutism, acne, and infertility. This research explores the impact of polycystic ovary syndrome on women's lives using photovoice. Nine participants photographed objects related to their quality of life and made diary entries explaining each photograph. Three themes emerged from thematic analysis of the diaries: control (of symptoms and polycystic ovary syndrome controlling their lives), perception (of self, others, and their situation), and support (from relationships, health care systems, and education). These findings illuminate positive aspects of living with polycystic ovary syndrome and the role pets and social networking sites play in providing support for women with polycystic ovary syndrome.

Autochthonous heritage languages and social media:writing and bilingual practices in Low German on Facebook

This article analyses how speakers of an autochthonous heritage language (AHL) make use of digital media, through the example of Low German, a regional language used by a decreasing number of speakers mainly in northern Germany. The focus of the analysis is on Web 2.0 and its interactive potential for individual speakers. The study therefore examines linguistic practices on the social network site Facebook, with special emphasis on language choice, bilingual practices and writing in the autochthonous heritage language. The findings suggest that social network sites such as Facebook have the potential to provide new mediatized spaces for speakers of an AHL that can instigate sociolinguistic change.

Footballers and social media ‘Faux Pas’:the Football Association’s cash cow?

Reflects on the increasing scope for irresponsible comments made on social media sites to attract civil or criminal penalties, and how such remarks by professional footballers have generated significant amounts of revenue for the Football Association (FA). Reviews the range of social media "faux pas" committed by footballers, the large fines imposed by the FA Regulatory Commission and why these constitute a useful "cash cow".

It's the way you check-in:identifying users in location-based social networks

In recent years, the rapid spread of smartphones has led to the increasing popularity of Location-Based Social Networks (LBSNs). Although a number of research studies and articles in the press have shown the dangers of exposing personal location data, the inherent nature of LBSNs encourages users to publish information about their current location (i.e., their check-ins). The same is true for the majority of the most popular social networking websites, which offer the possibility of associating the current location of users to their posts and photos. Moreover, some LBSNs, such as Foursquare, let users tag their friends in their check-ins, thus potentially releasing location information of individuals that have no control over the published data. This raises additional privacy concerns for the management of location information in LBSNs. In this paper we propose and evaluate a series of techniques for the identification of users from their check-in data. More specifically, we first present two strategies according to which users are characterized by the spatio-temporal trajectory emerging from their check-ins over time and the frequency of visit to specific locations, respectively. In addition to these approaches, we also propose a hybrid strategy that is able to exploit both types of information. It is worth noting that these techniques can be applied to a more general class of problems where locations and social links of individuals are available in a given dataset. We evaluate our techniques by means of three real-world LBSNs datasets, demonstrating that a very limited amount of data points is sufficient to identify a user with a high degree of accuracy. For instance, we show that in some datasets we are able to classify more than 80% of the users correctly.

Human-computer interaction and innovation in handheld, mobile and wearable technologies

Handheld and mobile technologies have witnessed significant advances in functionality, leading to their widespread use as both business and social networking tools. Human-Computer Interaction and Innovation in Handheld, Mobile and Wearable Technologies reviews concepts relating to the design, development, evaluation, and application of mobile technologies. Studies on mobile user interfaces, mobile learning, and mobile commerce contribute to the growing body of knowledge on this expanding discipline.

Analysis of constructivist, network-based discourses:concepts, prospects, and illustrations

The authors propose a new approach to discourse analysis which is based on meta data from social networking behavior of learners who are submerged in a socially constructivist e-learning environment. It is shown that traditional data modeling techniques can be combined with social network analysis - an approach that promises to yield new insights into the largely uncharted domain of network-based discourse analysis. The chapter is treated as a non-technical introduction and is illustrated with real examples, visual representations, and empirical findings. Within the setting of a constructivist statistics course, the chapter provides an illustration of what network-based discourse analysis is about (mainly from a methodological point of view), how it is implemented in practice, and why it is relevant for researchers and educators.

Relation discovery from web data for competency management

In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. We present a novel unsupervised machine learning method called CORDER (COmmunity Relation Discovery by named Entity Recognition) to turn these unstructured data into structured information for knowledge management in these organizations. CORDER exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments in an expert evaluation, a quantitative benchmarking, and an application of CORDER in a social networking tool called BuddyFinder.

A simple Bayesian modelling approach to event extraction from Twitter

With the proliferation of social media sites, social streams have proven to contain the most up-to-date information on current events. Therefore, it is crucial to extract events from the social streams such as tweets. However, it is not straightforward to adapt the existing event extraction systems since texts in social media are fragmented and noisy. In this paper we propose a simple and yet effective Bayesian model, called Latent Event Model (LEM), to extract structured representation of events from social media. LEM is fully unsupervised and does not require annotated data for training. We evaluate LEM on a Twitter corpus. Experimental results show that the proposed model achieves 83% in F-measure, and outperforms the state-of-the-art baseline by over 7%.© 2014 Association for Computational Linguistics.

Editorial

Full text: There are phrases in daily use today which were not so common a decade or so back, such as ‘ageing population’ or ‘climate changes’ or ‘emerging markets’ or even ‘social networking’. How do these things affect our lives is certainly interesting but for us as eye care practitioners how these changes affect our clinical work may be also relevant and sometimes more interesting. A recent advertisement for recruitment to the Royal Marine Corps of the British Army ended with a comment ‘find us on Facebook!’ The BCLA, IACLE and other organisations as well as many manufacturers have their own Facebook groups. In 2011 Chandni Thakkar was awarded the BCLA summer studentship and her project was based around increasing the contact lens business of a small independent optometric practice where contact lens sales were minimal. The practice typically recruited one new wearer per month. Chandni was able to increase the number of new patient fits with various strategies (her work was presented as poster at the 2012 BCLA conference in Birmingham). One of her strategies was to start a Facebook group and 655 joined the special group she started in just over a month. Interestingly she found that the largest single factor in convincing patients to trial contact lenses was recommendation by the eye care practitioner at the end of the examination, but nonetheless it is interesting that so many people used the social networking site to find out more information regarding contact lenses in her study. Moreover, we already see the use, by some practitioners, of smart phone ‘apps’ or electronic diaries or text messages when coordinating patient check-ups. Climate change has affected the way we think and act; we now leave out special recycle bins and we hope that the items that are recyclable are actually recycled and do not just join our other refuse somewhere down the track! How environmentally friendly are contact lenses? This was discussed by various speakers at this year's BCLA conference in Birmingham. Daily disposable lenses surely produce more contact lens waste but do not involve solutions in plastic bottles like monthly lenses. It is certainly something that manufacturers are taking seriously and of course there are environmental benefits but the cynic in each of us sees the marketing potential too. The way the ageing population is certainly something that will impact all healthcare providers. In the case of eye care with people living longer they will need refractive corrections for longer. Furthermore, since presbyopes are not resigning themselves to only gentle hobbies like knitting and gardening, but instead want to continue playing tennis or skiing or whatever, their visual demands are becoming more complex. This is certainly an area that contact lens manufacturers are focussing on (pun not intended!). Again the BCLA conference in Birmingham saw the launch of various new products by different companies to help us deal with our presbyopic contact lens wearers. It is great to have such choice and now with fitting methods becoming easier too we have no excuse not to try them out with our clients. Finally to emerging markets – well there was not a specific session at the BCLA conference in May discussing this but this most certainly would have been discussed by professional services managers and marketing directors of most of the contact lens companies. ‘How will we conquer China?’ ‘How can we increase our market share in Russia?’ Or ‘How should we spend our marketing budget in India?’ These topics as well as others would certainly have cropped up in backroom discussions. Certainly groups like IACLE (International Association of CL Educators) have increasing numbers of members and activities in developing markets to ensure that educators educate, to that practitioners can practice successfully and in turn patients can become successful contact lenses wearers. Companies also wish to increase their market share in these developing markets and from the point of view of CLAE we are certainly seeing more papers being submitted from these parts of the world. The traditional centres of knowledge are being challenged, I suppose as they have been throughout history, and this can only be a good thing for the pursuit of science. The BCLA conference in Birmingham welcomed more international visitors than ever, and from more countries, and long may that continue. Similarly, CLAE looks forward to a wider audience in years to come and a wider network of authors too.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.

Detecting expressions of blame or praise in text

The growth of social networking platforms has drawn a lot of attentions to the need for social computing. Social computing utilises human insights for computational tasks as well as design of systems that support social behaviours and interactions. One of the key aspects of social computing is the ability to attribute responsibility such as blame or praise to social events. This ability helps an intelligent entity account and understand other intelligent entities’ social behaviours, and enriches both the social functionalities and cognitive aspects of intelligent agents. In this paper, we present an approach with a model for blame and praise detection in text. We build our model based on various theories of blame and include in our model features used by humans determining judgment such as moral agent causality, foreknowledge, intentionality and coercion. An annotated corpus has been created for the task of blame and praise detection from text. The experimental results show that while our model gives similar results compared to supervised classifiers on classifying text as blame, praise or others, it outperforms supervised classifiers on more finer-grained classification of determining the direction of blame and praise, i.e., self-blame, blame-others, self-praise or praise-others, despite not using labelled training data.

Word of mouth communication within online communities:conceptualizing the online social network

Word of mouth (WOM) communication is a major part of online consumer interactions, particularly within the environment of online communities. Nevertheless, existing (offline) theory may be inappropriate to describe online WOM and its influence on evaluation and purchase.The authors report the results of a two-stage study aimed at investigating online WOM: a set of in-depth qualitative interviews followed by a social network analysis of a single online community. Combined, the results provide strong evidence that individuals behave as if Web sites themselves are primary "actors" in online social networks and that online communities can act as a social proxy for individual identification. The authors offer a conceptualization of online social networks which takes the Web site into account as an actor, an initial exploration of the concept of a consumer-Web site relationship, and a conceptual model of the online interaction and information evaluation process. © 2007 Wiley Periodicals, Inc. and Direct Marketing Educational Foundation, Inc.

Patterns of networking in the innovation process:a comparative study of the UK, Germany and Ireland

The research reported in this paper arose from collaboration with Brian Ashcroft (Fraser of Allander Institute, University of Strathclyde) and Stephen Roper (Northern Ireland Economic Research Centre, Queen's University of Belfast). The author is, however, solely responsible for the views expressed. The following sections are included: -Introduction -An Economics Perspective on Innovation Networks -The Product Development Survey -Discussion: Innovation, Networks and Institutions -Conclusions -References Read More: http://www.worldscientific.com/doi/abs/10.1142/9781848161481_0005