Cross-system recommendation: user-modelling via social media versus self-declared preferences

It is increasingly rare to encounter a Web service that doesn’t engage in some form of automated recommendation, with Collaborative Filtering (CF) techniques being virtually ubiquitous as the means for delivering relevant content. Yet several key issues still remain unresolved, including optimal handling of cold starts and how best to maintain user privacy within that context. Recent work has demonstrated a potentially fruitful line of attack in the form of cross system user modelling, which uses features generated from one domain to bootstrap recommendations in another. In this paper we evidence the effectiveness of this approach through direct real-world user feedback, deconstructing a cross-system news recommendation service where user models are generated via social media data. It is shown that even when a relatively naive vector-space approach is used, it is possible to automatically generate user-models that provide statistically superior performance than when items are explicitly filtered based on a user’s self-declared preferences. Detailed qualitative analysis of why such effects occur indicate that different models are capturing widely different areas within a user’s preference space, and that hybrid models represent fertile ground for future research

OSN Model For Business Growth Using Ecommerce Product Recommendation

Now A Days Online Shopping Has Achieved A Tremendous Popularity Within Very Less Amount Of Time. Recently Few Ecommerce Websites Has Been Developed Their Functionalities To A Extent Such That They Recommend The Product For Their Users Referring To The Connectivity Of The Users To The Social Media And Provide Direct Login From Such Social Media Such As Facebook, Twitter, Whatsapp. Recommend The Users That Are Totally New To The Website Client Novel Solution For Cross-Site Cold-Start Product Recommendation That Aims For Recommending Products From E-Commerce Websites. In Specific Propose Learning Both Users And Products Feature Representations From Data Collected From E-Commerce Websites Using Recurrent Top-K To Transform User’s Social Networking Features Into User Embeddings. The Survey Paper Develops A Top-K Approach Which Can Manipulate The Learnt User Implanting For Cold-Start Product Recommendation

Social Network Platform For Business Growth Using Ecommerce Product Recommendation

Present day's online shopping has accomplished an enormous disrepute privileged less measure of time. As of late few ecommerce sites has been created their functionalities to a point with the end goal that they suggest the product for their clients alluding to the availability of the clients to the social media and give coordinate login from such social media, (for example, facebook, Google+ ,and so forth). For suggesting the clients that are absolutely new to the sites, we utilize novel answer for cross-webpage cold-start product recommendation that goes for prescribing products from online business sites. In particular, we propose learning the two clients and products include portrayals from information gathered from internet business sites utilizing repetitive Matrix Factorization to change client's social networking highlights into client embeddings. We at that point build up a feature-based matrix factorization approach which can control the learnt client embedding for cold-start product recommendation

Parental psychological problems were associated with higher screen time and the use of mature-rated media in children

Publisher Copyright: © 2022 The Authors. Acta Paediatrica published by John Wiley & Sons Ltd on behalf of Foundation Acta Paediatrica.Aim: Parents’ psychological problems may affect children's screen time, but research has been scarce. We examined the association between parental psychological problems and children's screen media behaviours in a nationally representative sample. Methods: The participants were from the Adolescent Brain Cognitive Development study, recruited by probability sampling from the USA population. Children reported their use of TV, videos, video games, social media and mature-rated media. The parents (85% mothers) reported psychological problems using the Adult Self-Report questionnaire. Results: In 10,650 children (5112 girls, 5538 boys) aged 9.9 ± 0.6 years, the presence of parental psychological problems was associated with children spending more daily time on screen media and with meeting the recommendation of ≤2 daily hours less often than children whose parents did not have psychological problems. Parental psychological problems were associated with children's TV watching, video watching and gaming but not with using social media. Parental internalising problems were associated with children watching mature-rated movies (odds ratio [OR] = 1.14, 95% confidence interval [CI] = 1.00, 1.30) and playing mature-rated games (OR = 1.27, 95% CI = 1.11, 1.45). Conclusion: Presence of parental psychological problems is associated with higher screen time and use of mature-rated media in children. This cross-sectional study was not able to examine causal associations.Peer reviewe

Cross-Modal Health State Estimation

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geospatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.Comment: Accepted to ACM Multimedia 2018 Conference - Brave New Ideas, Seoul, Korea, ACM ISBN 978-1-4503-5665-7/18/1