Modern online services continuously generate data at very fast rates. This continuous flow of data encompasses content -- e.g., posts, news, products, comments --, but also user feedback -- e.g., ratings, views, reads, clicks --, together with context data -- user device, spatial or temporal data, user task or activity, weather. This can be overwhelming for systems and algorithms designed to train in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate online methods able to transparently adapt to the inherent dynamics of online services. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with the continuous flows of data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online. The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, and their implications regarding multiple dimensions, such as evaluation, reproducibility, privacy and explainability.

Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data. Due to the pervasiveness of recommender systems in online technologies, researchers have carried out several robustness studies focusing on data sparsity and profile injection attacks. Instead, we propose a more holistic view of robustness for recommender systems that encompasses multiple dimensions - robustness with respect to sub-populations, transformations, distributional disparity, attack, and data sparsity. While there are several libraries that allow users to compare different recommender system models, there is no software library for comprehensive robustness evaluation of recommender system models under different scenarios. As our main contribution, we present a robustness evaluation toolkit, Robustness Gym for RecSys (RGRecSys -- https://www.github.com/salesforce/RGRecSys), that allows us to quickly and uniformly evaluate the robustness of recommender system models.

Lorry driver Michael Hosty and another man, Ryan Campbell, were commended for their bravery in rushing to help pull Onut free from his burning cab. Mr Hosty, who now has post-traumatic stress disorder, recalled grabbing him and saying: "Look, mate, if you don't help me out we are both going to die."

Just because you can do something doesn't always mean you should. Embedding all of your application logic directly in the database can make tracking migrations and releases difficult. At the same time, a complex pipeline that takes a nightly extract, loads something into Spark, generates results, that you then feed back into the database isn't exactly lightweight. In the case of plpython3u and pandas, scheduling something like the above to run daily with pg_cron could be a much simpler solution. With a mix of SciPy, NumPy and Pandas there is a lot of interesting potential here and I'd love to hear what practical uses others come up with @crunchydata, or give it yourself a try-our database-as-a-service Crunchy Bridge comes already preconfigured with plpython3u and SciPy, NumPy, and Pandas.

While during the 70s, the funding for research in A.I. had severely reduced both in the United Kingdom and the United States owing to political criticism, computers had begun to become faster and more capable of performing multiple functions better. Despite the reduction of funds in the field of A.I., it thrived in terms of performance in the 90s. Furthermore, in 1997, for the very first time, IBM's Deep Blue, won against a reigning world champion. In subsequent years, toy robots too were introduced into the market, including Furby and Sony's robotic dog AIBO. Over the years, A.I. has accomplished similar feats. In recent times, we've had virtual assistants such as Siri, Cortana, Alexa as well as the controversial development of humanoid robot Sophia, who's considered to be the very first robotic citizen.

There is a knowledge gap regarding which types of failures robots undergo in domestic settings and how these failures influence customer experience. We classified 10,072 customer reviews of small utilitarian domestic robots on Amazon by the robotic failures described in them, grouping failures into twelve types and three categories (Technical, Interaction, and Service). We identified sources and types of failures previously overlooked in the literature, combining them into an updated failure taxonomy. We analyzed their frequencies and relations to customer star ratings. Results indicate that for utilitarian domestic robots, Technical failures were more detrimental to customer experience than Interaction or Service failures. Issues with Task Completion and Robustness & Resilience were commonly reported and had the most significant negative impact. Future failure-prevention and response strategies should address the technical ability of the robot to meet functional goals, operate and maintain structural integrity over time. Usability and interaction design were less detrimental to customer experience, indicating that customers may be more forgiving of failures that impact these aspects for the robots and practical uses examined. Further, we developed a Natural Language Processing model capable of predicting whether a customer review contains content that describes a failure and the type of failure it describes. With this knowledge, designers and researchers of robotic systems can prioritize design and development efforts towards essential issues.

Many researchers have used tag information to improve the performance of recommendation techniques in recommender systems. Examining the tags of users will help to get their interests and leads to more accuracy in the recommendations. Since user-defined tags are chosen freely and without any restrictions, problems arise in determining their exact meaning and the similarity of tags. On the other hand, using thesauruses and ontologies to find the meaning of tags is not very efficient due to their free definition by users and the use of different languages in many data sets. Therefore, this article uses the mathematical and statistical methods to determine lexical similarity and co-occurrence tags solution to assign semantic similarity. On the other hand, due to the change of users' interests over time this article have considered the time of tag assignments in co-occurrence tags for determined similarity of tags. Then the graph is created based on these similarities. For modeling the interests of the users, the communities of tags are determined by using community detection methods. So recommendations based on the communities of tags and similarity between resources are done. The performance of the proposed method has been done using two criteria of precision and recall based on evaluations with "Delicious" dataset. The evaluation results show that, the precision and recall of the proposed method have significantly improved, compared to the other methods.

Recommender Systems (RSs) aim to model and predict the user preference while interacting with items, such as Points of Interest (POIs). These systems face several challenges, such as data sparsity, limiting their effectiveness. In this paper, we address this problem by incorporating social, geographical, and temporal information into the Matrix Factorization (MF) technique. To this end, we model social influence based on two factors: similarities between users in terms of common check-ins and the friendships between them. We introduce two levels of friendship based on explicit friendship networks and high check-in overlap between users. We base our friendship algorithm on users' geographical activity centers. The results show that our proposed model outperforms the state-of-the-art on two real-world datasets. More specifically, our ablation study shows that the social model improves the performance of our proposed POI recommendation system by 31% and 14% on the Gowalla and Yelp datasets in terms of Precision@10, respectively.

Crime Stoppers of Houston Andy Kahan and FOP national vice president Joe Gamaldi react to the nation's growing crime crisis on'Justice w/ Judge Jeanine.' A potential fifth victim has been identified in the "shopping cart killer" case, involving an alleged serial killer in Northern Virginia, that has crime experts warning of the dangers of online dating. Officers believe suspect Anthony Robinson made contact with the victims via dating websites which Crime Stoppers of Houston's Andy Kahan described on "Justice w/ Judge Jeanine" as "toy stores" for murderers. "The dark side of online dating apps are luring in millions of women to, perhaps… mortal danger," he said. "There are no background checks; we all know sex offenders troll it. You're essentially playing Russian roulette with your life when you divulge personal information and continue to go out and meet people that you do not know."

As a marketer or a business owner in today's digital world, nothing is more important than forecasting. Knowing the marketing industry, the trends, and the issues that you will face gives you a great competitive advantage. Marketers should know how to stay up to date on new trends and the constantly changing marketing landscape. Forecasting the future of marketing and being the first to give the people what they really want will take you nowhere but the top -- Exactly where you belong. Is there anything better than that?