Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area.

Model evolution and constant availability of data are two common phenomena in large-scale real-world machine learning applications, e.g. ads and recommendation systems. To adapt, the real-world system typically retrain with all available data and online learn with recently available data to update the models periodically with the goal of better serving performance. In this paper, we propose a novel framework, named Confidence Ranking, which designs the optimization objective as a ranking function with two different models. Our confidence ranking loss allows direct optimization of the logits output for different convex surrogate functions of metrics, e.g. AUC and Accuracy depending on the target task and dataset. Armed with our proposed methods, our experiments show that the introduction of confidence ranking loss can outperform all baselines on the CTR prediction tasks of public and industrial datasets. This framework has been deployed in the advertisement system of JD.com to serve the main traffic in the fine-rank stage.

In the recommender system community, this situation is known as the "cold-start" problem [7, 9], where rare ('cold') entities (e.g., We present Variational Bayesian Network (VBN) - a novel Bayesian unpopular items or new items that are introduced to the catalog) are entity representation learning model that utilizes hierarchical and often poorly represented due to insufficient statistics. In the natural relational side information and is particularly useful for modeling language processing community, where the focus is on learning entities in the "long-tail", where the data is scarce. VBN provides representations for words and phrases, a common mitigation is to better modeling for long-tail entities via two complementary mechanisms: increase the training set size by utilizing increasingly larger corpus First, VBN employs informative hierarchical priors that e.g., BERT [20, 39]. However, it was shown that even when enable information propagation between entities sharing common increasing the amount of co-occurrence data, the existence of rare, ancestors. Additionally, VBN models explicit relations between entities out-of-vocabulary entities persists [26, 50, 52, 53].

The widespread diffusion of connected smart devices has contributed to the rapid expansion and evolution of the Internet at its edge. Personal mobile devices interact with other smart objects in their surroundings, adapting behavior based on rapidly changing user context. The ability of mobile devices to process this data locally is crucial for quick adaptation. This can be achieved through a single elaboration process integrated into user applications or a middleware platform for context processing. However, the lack of public datasets considering user context complexity in the mobile environment hinders research progress. We introduce MyDigitalFootprint, a large-scale dataset comprising smartphone sensor data, physical proximity information, and Online Social Networks interactions. This dataset supports multimodal context recognition and social relationship modeling. It spans two months of measurements from 31 volunteer users in their natural environment, allowing for unrestricted behavior. Existing public datasets focus on limited context data for specific applications, while ours offers comprehensive information on the user context in the mobile environment. To demonstrate the dataset's effectiveness, we present three context-aware applications utilizing various machine learning tasks: (i) a social link prediction algorithm based on physical proximity data, (ii) daily-life activity recognition using smartphone-embedded sensors data, and (iii) a pervasive context-aware recommender system. Our dataset, with its heterogeneity of information, serves as a valuable resource to validate new research in mobile and edge computing.

Probabilistic user modeling is essential for building machine learning systems in the ubiquitous cases with humans in the loop. However, modern advanced user models, often designed as cognitive behavior simulators, are incompatible with modern machine learning pipelines and computationally prohibitive for most practical applications. We address this problem by introducing widely-applicable differentiable surrogates for bypassing this computational bottleneck; the surrogates enable computationally efficient inference with modern cognitive models. We show experimentally that modeling capabilities comparable to the only available solution, existing likelihood-free inference methods, are achievable with a computational cost suitable for online applications. Finally, we demonstrate how AI-assistants can now use cognitive models for online interaction in a menu-search task, which has so far required hours of computation during interaction.

Holographic Intellectual Voice Assistant (HIVA) aims to facilitate human computer interaction using audiovisual effects and 3D avatar. HIVA provides complete information about the university, including requests of various nature: admission, study issues, fees, departments, university structure and history, canteen, human resources, library, student life and events, information about the country and the city, etc. There are other ways for receiving the data listed above: the university's official website and other supporting apps, HEI (Higher Education Institution) official social media, directly asking the HEI staff, and other channels. However, HIVA provides the unique experience of "face-to-face" interaction with an animated 3D mascot, helping to get a sense of 'real-life' communication. The system includes many sub-modules and connects a family of applications such as mobile applications, Telegram chatbot, suggestion categorization, and entertainment services. The Voice assistant uses Russian language NLP models and tools, which are pipelined for the best user experience.

Review-Based Recommender Systems (RBRS) have attracted increasing research interest due to their ability to alleviate well-known cold-start problems. RBRS utilizes reviews to construct the user and items representations. However, in this paper, we argue that such a reliance on reviews may instead expose systems to the risk of being shilled. To explore this possibility, in this paper, we propose the first generation-based model for shilling attacks against RBRSs. Specifically, we learn a fake review generator through reinforcement learning, which maliciously promotes items by forcing prediction shifts after adding generated reviews to the system. By introducing the auxiliary rewards to increase text fluency and diversity with the aid of pre-trained language models and aspect predictors, the generated reviews can be effective for shilling with high fidelity. Experimental results demonstrate that the proposed framework can successfully attack three different kinds of RBRSs on the Amazon corpus with three domains and Yelp corpus. Furthermore, human studies also show that the generated reviews are fluent and informative. Finally, equipped with Attack Review Generators (ARGs), RBRSs with adversarial training are much more robust to malicious reviews.

Feature interactions can play a crucial role in recommendation systems as they capture complex relationships between user preferences and item characteristics. Existing methods such as Deep & Cross Network (DCNv2) may suffer from high computational requirements due to their cross-layer operations. In this paper, we propose a novel approach called blockwise feature interaction (BFI) to help alleviate this issue. By partitioning the feature interaction process into smaller blocks, we can significantly reduce both the memory footprint and the computational burden. Four variants (denoted by P, Q, T, S, respectively) of BFI have been developed and empirically compared. Our experimental results demonstrate that the proposed algorithms achieves close accuracy compared to the standard DCNv2, while greatly reducing the computational overhead and the number of parameters. This paper contributes to the development of efficient recommendation systems by providing a practical solution for improving feature interaction efficiency.

Log Off Movement CEO Emma Lembke and teacher Matt Miles discuss the impact of artificial intelligence on kids on'The Story.' More people are turning to dating apps to find a match, but one company is taking it a step further by using artificial intelligence (AI) to fuel a more efficient and personalized version of online dating, according to Lior Baruch, the co-founder and CEO AlgoAI Tech. "Maybe it was a website in the past, now it's an app, but it's kind of the same," Baruch told Fox News Digital of the traditional form of online dating. "You go into a website to type few details about yourself, they ask you a few questions, you answer them. You get either one, two, three options, or you see tons of options in front of you that you just choose from, like it's kind of a meat market. If you're not, people can stay there for years and I'm not exaggerating."

Background: People's health depends on the use of proper diet as an important factor. Today, with the increasing mechanization of people's lives, proper eating habits and behaviors are neglected. On the other hand, food recommendations in the field of health have also tried to deal with this issue. But with the introduction of the Western nutrition style and the advancement of Western chemical medicine, many issues have emerged in the field of disease treatment and nutrition. Recent advances in technology and the use of artificial intelligence methods in information systems have led to the creation of recommender systems in order to improve people's health. Methods: A hybrid recommender system including, collaborative filtering, content-based, and knowledge-based models was used. Machine learning models such as Decision Tree, k-Nearest Neighbors (kNN), AdaBoost, and Bagging were investigated in the field of food recommender systems on 2519 students in the nutrition management system of a university. Student information including profile information for basal metabolic rate, student reservation records, and selected diet type is received online. Among the 15 features collected and after consulting nutrition experts, the most effective features are selected through feature engineering. Using machine learning models based on energy indicators and food selection history by students, food from the university menu is recommended to students. Results: The AdaBoost model has the highest performance in terms of accuracy with a rate of 73.70 percent. Conclusion: Considering the importance of diet in people's health, recommender systems are effective in obtaining useful information from a huge amount of data. Keywords: Recommender system, Food behavior and habits, Machine learning, Classification