A single man searching for love in Miami is confused. On the one hand, he received a barrage of criticism online for asking to split the bill on a first date with a girl he met on Tinder, an online dating app. On the other hand, he thinks – with modern-day feminism strongly in place in 2024 – women want equality and all that comes with it. The single man, who goes by "Water Boy" (@TheWaterBoy) on TikTok, posted a video recording of the date on TikTok. Users posting retellings of their bad dates are commonplace on the platform.

Recommender systems are often susceptible to well-crafted fake profiles, leading to biased recommendations. Among existing defense methods, data-processing-based methods inevitably exclude normal samples, while model-based methods struggle to enjoy both generalization and robustness. To this end, we suggest integrating data processing and the robust model to propose a general framework, Triple Cooperative Defense (TCD), which employs three cooperative models that mutually enhance data and thereby improve recommendation robustness. Furthermore, Considering that existing attacks struggle to balance bi-level optimization and efficiency, we revisit poisoning attacks in recommender systems and introduce an efficient attack strategy, Co-training Attack (Co-Attack), which cooperatively optimizes the attack optimization and model training, considering the bi-level setting while maintaining attack efficiency. Moreover, we reveal a potential reason for the insufficient threat of existing attacks is their default assumption of optimizing attacks in undefended scenarios. This overly optimistic setting limits the potential of attacks. Consequently, we put forth a Game-based Co-training Attack (GCoAttack), which frames the proposed CoAttack and TCD as a game-theoretic process, thoroughly exploring CoAttack's attack potential in the cooperative training of attack and defense. Extensive experiments on three real datasets demonstrate TCD's superiority in enhancing model robustness. Additionally, we verify that the two proposed attack strategies significantly outperform existing attacks, with game-based GCoAttack posing a greater poisoning threat than CoAttack.

Multi-Objective Recommender Systems (MORSs) emerged as a paradigm to guarantee multiple (often conflicting) goals. Besides accuracy, a MORS can operate at the global level, where additional beyond-accuracy goals are met for the system as a whole, or at the individual level, meaning that the recommendations are tailored to the needs of each user. The state-of-the-art MORSs either operate at the global or individual level, without assuming the co-existence of the two perspectives. In this study, we show that when global and individual objectives co-exist, MORSs are not able to meet both types of goals. To overcome this issue, we present an approach that regulates the recommendation lists so as to guarantee both global and individual perspectives, while preserving its effectiveness. Specifically, as individual perspective, we tackle genre calibration and, as global perspective, provider fairness. We validate our approach on two real-world datasets, publicly released with this paper.

GNN-based recommenders have excelled in modeling intricate user-item interactions through multi-hop message passing. However, existing methods often overlook the dynamic nature of evolving user-item interactions, which impedes the adaption to changing user preferences and distribution shifts in newly arriving data. Thus, their scalability and performances in real-world dynamic environments are limited. In this study, we propose GraphPro, a framework that incorporates parameter-efficient and dynamic graph pre-training with prompt learning. This novel combination empowers GNNs to effectively capture both long-term user preferences and short-term behavior dynamics, enabling the delivery of accurate and timely recommendations. Our GraphPro framework addresses the challenge of evolving user preferences by seamlessly integrating a temporal prompt mechanism and a graph-structural prompt learning mechanism into the pre-trained GNN model. The temporal prompt mechanism encodes time information on user-item interaction, allowing the model to naturally capture temporal context, while the graph-structural prompt learning mechanism enables the transfer of pre-trained knowledge to adapt to behavior dynamics without the need for continuous incremental training. We further bring in a dynamic evaluation setting for recommendation to mimic real-world dynamic scenarios and bridge the offline-online gap to a better level. Our extensive experiments including a large-scale industrial deployment showcases the lightweight plug-in scalability of our GraphPro when integrated with various state-of-the-art recommenders, emphasizing the advantages of GraphPro in terms of effectiveness, robustness and efficiency.

Cross-domain recommendation (CDR) is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existing cross-domain recommendations fail to fully utilize the target domain's special features and are hard to be generalized to new domains. The designed network is complex and is not suitable for rapid industrial deployment. Our method introduces a two-step domain-aware cross-attention, extracting transferable features of the source domain from different granularity, which allows the efficient expression of both domain and user interests. In addition, we simplify the training process, and our model can be easily deployed on new domains. We conduct experiments on both public datasets and industrial datasets, and the experimental results demonstrate the effectiveness of our method. We have also deployed the model in an online advertising system and observed significant improvements in both Click-Through-Rate (CTR) and effective cost per mille (ECPM).

Graph-based collaborative filtering methods have prevailing performance for recommender systems since they can capture high-order information between users and items, in which the graphs are constructed from the observed user-item interactions that might miss links or contain spurious positive interactions in industrial scenarios. The Bayesian Graph Neural Network framework approaches this issue with generative models for the interaction graphs. The critical problem is to devise a proper family of graph generative models tailored to recommender systems. We propose an efficient generative model that jointly considers the preferences of users, the concurrence of items and some important graph structure information. Experiments on four popular benchmark datasets demonstrate the effectiveness of our proposed graph generative methods for recommender systems.

Amazon upgraded its Echo Show 8 display late last year to give it a sleeker design and faster Alexa responses, and you can get it right now at the lowest price we've seen it hit. The third-gen, 2023 Echo Show 8 is 40 percent off on Amazon, bringing it down to just 90. The display comes in two colors, Charcoal and Glacier White, and the discount applies to both. Get the latest Echo Show 8 for 40 percent off. The 2023 Echo Show 8 brought upgrades inside and out to the smart home gadget. It has spatial audio with room calibration that should make for much fuller sound than the previous models were able to achieve.

Deep learning-based (DL) models in recommender systems (RecSys) have gained significant recognition for their remarkable accuracy in predicting user preferences. However, their performance often lacks a comprehensive evaluation from a human-centric perspective, which encompasses various dimensions beyond simple interest matching. In this work, we have developed a robust human-centric evaluation framework that incorporates seven diverse metrics to assess the quality of recommendations generated by five recent open-sourced DL models. Our evaluation datasets consist of both offline benchmark data and personalized online recommendation feedback collected from 445 real users. We find that (1) different DL models have different pros and cons in the multi-dimensional metrics that we test with; (2) users generally want a combination of accuracy with at least one another human values in the recommendation; (3) the degree of combination of different values needs to be carefully experimented to user preferred level.

The digital age is marked by an extraordinary growth in connected devices, leading to a massive influx of data through the Internet [12]. This data is primarily managed by cloud infrastructures. The proliferation of smart devices such as smartphones, tablets, smartwatches, and fitness trackers has transformed them into essential aspects of daily life [8]. These devices accumulate extensive contextual information about users, encompassing their location, activities, and environmental conditions [5]. This information is crucial for applications in predicting user behavior and providing personalized experiences. Mobile crowdsourcing has emerged as a significant phenomenon, where individuals collectively contribute data through various digital channels [32]. Applications in this domain, like traffic monitoring systems, utilize crowd-sourced data to offer real-time insights. However, the process often raises concerns about the privacy of individual contributors. The transparency in data usage and the potential risk of sensitive information being accessed by unauthorized entities are issues that need addressing [11, 26].

Three people have been jailed in the kidnapping and killing of a Hmong American comedian and activist who was found dead near Medellín after going out to meet a woman he reportedly met on social media, Colombian officials announced Thursday. The Prosecutor's Office said in a statement that two men and a woman were charged with the crimes of aggravated kidnapping for extortion and aggravated homicide in the death last month of Tou Ger Xiong, 50. The suspects denied the charges at a hearing, the statement said. A minor who presented himself to the Public Prosecutor's Office admitting to having participated in the crime also was charged in the case and transferred to a special detention center for minors, it added. The U.S. Embassy in Bogota warned a week ago about Colombian criminals who use dating apps to lure victims and then assault and rob them.