This is a post-peer-review version of an article published in Applied Soft Computing . This manuscript is made available under the Elsevier user license. Published in: Applied Soft Computing, 2025. Abstract Over the past decade, recommender systems have experienced a surge in popularity. Despite notable progress, they grapple with challenging issues, such as high data dimensionality and sparseness. Representing users and items as low-dimensional embeddings learned via neural networks has become a leading solution. However, while recent studies show promising results, many approaches rely on complex architectures or require content data, which may not always be available. This paper presents Interact2Vec, a novel neural network-based model that simultaneously learns distributed embeddings for users and items while demanding only implicit feedback. The model employs state-of-the-art strategies that natural language processing models commonly use to optimize the training phase and enhance the final embeddings. Two types of experiments were conducted regarding the extrinsic and intrinsic quality of the model. In the former, we benchmarked the recommendations generated by Interact2Vec's embeddings in a top-N ranking problem, comparing them with six other recommender algorithms. The model achieved the second or third-best results in 30% of the datasets, being competitive with other recommenders, and has proven to be very efficient with an average training time reduction of 274% compared to other embedding-based models. Later, we analyzed the intrinsic quality of the embeddings through similarity tables. Our findings suggest that Interact2Vec can achieve promising results, especially on the extrinsic task, and is an excellent embedding-generator model for scenarios of scarce computing resources, enabling the learning of item and user embeddings simultaneously and efficiently. Keywords: recommender systems, collaborative filtering, distributed vector representation, embeddings1. Introduction As technology advances and content becomes increasingly accessible, a growing volume of data is generated and shared daily. While this has led to numerous advancements in the modern world, the sheer magnitude of information means that only a fraction is relevant to individual users.

A Whittier man was arrested Thursday for allegedly using dating apps such as Tinder, Hinge and Bumble to con people out of more than 2 million, according to authorities. Christopher Earl Lloyd, 39, was charged with 13 counts of wire fraud and one count of engaging in a monetary transaction in property derived from fraud, according to the U.S. attorney's office for the Central District of California. If convicted, he faces a maximum possible sentence of 20 years in federal prison for each wire fraud count and up to 10 years for the monetary transaction count. Between April 2021 and February 2024, authorities say Lloyd used dating apps and websites to find and contact alleged victims, lying about his financial success and knowledge in investing. Prosecutors also allege Lloyd lied about being a financial manager, the vice president of a company called Planet 13 Holdings and that he worked for an investment company called Landmark Associates.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A California man was federally charged for allegedly scamming more than 2 million from people over popular dating apps by posing as someone who was "financially successful and knowledgeable about investments," prosecutors said. Christopher Earl Lloyd, 39, of Whittier, is now facing a 14-count federal indictment in connection with the alleged scheme he carried out for nearly three years on dating apps such as Tinder, Hinge and Bumble, according to the U.S. Attorney's Office of the Central District of California. "According to the indictment that a federal grand jury returned on July 2, from April 2021 to February 2024, Lloyd used dating apps and websites to befriend and engage in romantic relationships with his victims. Lloyd lied to his victims to give them the impression that he was financially successful and knowledgeable about investments," the Attorney's Office said.

In Conversational Recommendation Systems (CRS), a user provides feedback on recommended items at each turn, leading the CRS towards improved recommendations. Due to the need for a large amount of data, a user simulator is employed for both training and evaluation. Such user simulators critique the current retrieved item based on knowledge of a single target item. However, system evaluation in offline settings with simulators is limited by the focus on a single target item and their unlimited patience over a large number of turns. To overcome these limitations of existing simulators, we propose Fashion-AlterEval, a new dataset that contains human judgments for a selection of alternative items by adding new annotations in common fashion CRS datasets. Consequently, we propose two novel meta-user simulators that use the collected judgments and allow simulated users not only to express their preferences about alternative items to their original target, but also to change their mind and level of patience. In our experiments using the Shoes and Fashion IQ as the original datasets and three CRS models, we find that using the knowledge of alternatives by the simulator can have a considerable impact on the evaluation of existing CRS models, specifically that the existing single-target evaluation underestimates their effectiveness, and when simulatedusers are allowed to instead consider alternative relevant items, the system can rapidly respond to more quickly satisfy the user.

Nope, it's not just you: Reports of Google Assistant strugglng to perform even basic smart home commands have been surging in recent weeks, and now Google is admitting that something's amiss. The lead executive for Google's Home and Nest division tweeted on X that he's heard the complaints "loud and clear" and revealed that his team is "actively working on major improvements." "I want to acknowledge the recent feedback about Google Assistant reliability on our home devices," said Anish Kattukaran, the director of product management for Google Home and Nest. "I sincerely apologize for what you're experiencing and feeling!" Kattukaran's assurances come after a steep rise in complaints about Google Assistant on Google's Nest speakers and displays. Some users have been reporting that their Assistant routines have stopped working, while others say their Assistant-enabled devices have lost contact with smart lights, fail to play Spotify playlists, or can no longer control their Chromecast streaming devices with voice commands.

The matrix contextual bandit (CB), as an extension of the well-known multi-armed bandit, is a powerful framework that has been widely applied in sequential decision-making scenarios involving low-rank structure. In many real-world scenarios, such as online advertising and recommender systems, additional graph information often exists beyond the low-rank structure, that is, the similar relationships among users/items can be naturally captured through the connectivity among nodes in the corresponding graphs. However, existing matrix CB methods fail to explore such graph information, and thereby making them difficult to generate effective decision-making policies. T o fill in this void, we propose in this paper a novel matrix CB algorithmic framework that builds upon the classical upper confidence bound (UCB) framework. This new framework can effectively integrate both the low-rank structure and graph information in a unified manner. Specifically, it involves first solving a joint nuclear norm and matrix Laplacian regularization problem, followed by the implementation of a graph-based generalized linear version of the UCB algorithm. Rigorous theoretical analysis demonstrates that our procedure outperforms several popular alternatives in terms of cumulative regret bound, owing to the effective utilization of graph information. A series of synthetic and real-world data experiments are conducted to further illustrate the merits of our procedure.

Recommenders are significantly shaping online information consumption. While effective at personalizing content, these systems increasingly face criticism for propagating irrelevant, unwanted, and even harmful recommendations. Such content degrades user satisfaction and contributes to significant societal issues, including misinformation, radicalization, and erosion of user trust. Although platforms offer mechanisms to mitigate exposure to undesired content, these mechanisms are often insufficiently effective and slow to adapt to users' feedback. This paper introduces an intuitive, model-agnostic, and distribution-free method that uses conformal risk control to provably bound unwanted content in personalized recommendations by leveraging simple binary feedback on items. We also address a limitation of traditional conformal risk control approaches, i.e., the fact that the recommender can provide a smaller set of recommended items, by leveraging implicit feedback on consumed items to expand the recommendation set while ensuring robust risk mitigation. Our experimental evaluation on data coming from a popular online video-sharing platform demonstrates that our approach ensures an effective and controllable reduction of unwanted recommendations with minimal effort. The source code is available here: https://github.com/geektoni/mitigating-harm-recsys.

Personalized News Recommendation systems (PNR) have emerged as a solution to information overload by predicting and suggesting news items tailored to individual user interests. However, traditional PNR systems face several challenges, including an overreliance on textual content, common neglect of short-term user interests, and significant privacy concerns due to centralized data storage. This paper addresses these issues by introducing a novel multimodal federated learning-based approach for news recommendation. First, it integrates both textual and visual features of news items using a multimodal model, enabling a more comprehensive representation of content. Second, it employs a time-aware model that balances users' long-term and short-term interests through multi-head self-attention networks, improving recommendation accuracy. Finally, to enhance privacy, a federated learning framework is implemented, enabling collaborative model training without sharing user data. The framework divides the recommendation model into a large server-maintained news model and a lightweight user model shared between the server and clients. The client requests news representations (vectors) and a user model from the central server, then computes gradients with user local data, and finally sends their locally computed gradients to the server for aggregation. The central server aggregates gradients to update the global user model and news model. The updated news model is further used to infer news representation by the server. To further safeguard user privacy, a secure aggregation algorithm based on Shamir's secret sharing is employed. Experiments on a real-world news dataset demonstrate strong performance compared to existing systems, representing a significant advancement in privacy-preserving personalized news recommendation.

Recent advances in citation recommendation have improved accuracy by leveraging multi-view representation learning to integrate the various modalities present in scholarly documents. However, effectively combining multiple data views requires fusion techniques that can capture complementary information while preserving the unique characteristics of each modality. We propose a novel citation recommendation algorithm that improves upon linear Canonical Correlation Analysis (CCA) methods by applying Deep CCA (DCCA), a neural network extension capable of capturing complex, non-linear relationships between distributed textual and graph-based representations of scientific articles. Experiments on the large-scale DBLP (Digital Bibliography & Library Project) citation network dataset demonstrate that our approach outperforms state-of-the-art CCA-based methods, achieving relative improvements of over 11% in Mean Average Precision@10, 5% in Precision@10, and 7% in Recall@10. These gains reflect more relevant citation recommendations and enhanced ranking quality, suggesting that DCCA's non-linear transformations yield more expressive latent representations than CCA's linear projections.

Last week, Radio 4's Woman's Hour talked about the role of women in the video games industry. It featured interviews with gaming insiders, from esports presenter Frankie Ward to members of the inclusive online community Black Girl Gamers. It was wonderful to hear so many disparate, expert views on games culture being given so much time on the show. One of my favourite moments was when presenter Nuala McGovern read out some listener responses to the question: why do you play video games? "I don't think there's enough recognition of gaming as an activity for couples," one replied.