When it comes to online dating, Giovanni Wolfram, a 25-year-old living in Santa Fe, New Mexico, isn't all too worried about whether his fellow dating app users will find him attractive. Rather, his biggest fear is that he might come off as "cringey." "You can get away with being ugly," Wolfram says. "But being cringey is just like--that's a character that's imprinted on you." Since he first joined Hinge at 18, he has worked hard to scrub his profile of sincerity.

As recommender systems become increasingly complex, transparency is essential to increase user trust, accountability, and regulatory compliance. Neuro-symbolic approaches that integrate symbolic reasoning with sub-symbolic learning offer a promising approach toward transparent and user-centric systems. In this work-in-progress, we investigate using fuzzy neural networks (FNNs) as a neuro-symbolic approach for recommendations that learn logic-based rules over predefined, human-readable atoms. Each rule corresponds to a fuzzy logic expression, making the recommender's decision process inherently transparent. In contrast to black-box machine learning methods, our approach reveals the reasoning behind a recommendation while maintaining competitive performance. We evaluate our method on a synthetic and MovieLens 1M datasets and compare it to state-of-the-art recommendation algorithms. Our results demonstrate that our approach accurately captures user behavior while providing a transparent decision-making process. Finally, the differentiable nature of this approach facilitates an integration with other neural models, enabling the development of hybrid, transparent recommender systems.

This study focuses on the problem of path modeling in heterogeneous information networks and proposes a multi-hop path-aware recommendation framework. The method centers on multi-hop paths composed of various types of entities and relations. It models user preferences through three stages: path selection, semantic representation, and attention-based fusion. In the path selection stage, a path filtering mechanism is introduced to remove redundant and noisy information. In the representation learning stage, a sequential modeling structure is used to jointly encode entities and relations, preserving the semantic dependencies within paths. In the fusion stage, an attention mechanism assigns different weights to each path to generate a global user interest representation. Experiments conducted on real-world datasets such as Amazon-Book show that the proposed method significantly outperforms existing recommendation models across multiple evaluation metrics, including HR@10, Recall@10, and Precision@10. The results confirm the effectiveness of multi-hop paths in capturing high-order interaction semantics and demonstrate the expressive modeling capabilities of the framework in heterogeneous recommendation scenarios. This method provides both theoretical and practical value by integrating structural information modeling in heterogeneous networks with recommendation algorithm design. It offers a more expressive and flexible paradigm for learning user preferences in complex data environments.

Get lifetime access to this AI tutor app, SpeedTutorAI, for 29.97 for a limited time (reg. SpeedTutorAI is an AI-powered homework helper and study assistant that acts like a personal tutor on your iPhone or iPad. Unlike basic virtual assistants like Siri, this app goes further, offering support with math problems, lecture summaries, and even complex topic explanations. Whether you're cramming for finals or just need quick clarification on a tough concept, this app is built to help. If you've ever tried recording a class on your phone only to never revisit the audio file, SpeedTutorAI offers a smarter solution.

In January, Apple agreed to pay out 95 million to settle a class action lawsuit over claims its voice assistant Siri listened in on private conversations. Now, affected users have less than eight weeks to stake their claim to a slice of the cash. The Lopez v Apple Inc. lawsuit was filed back in December, accusing Apple of recording private conversations as a result of unintended Siri activations, and then sharing that data with third parties. Two plaintiffs claim they had related advertisements served to them after having personal conversations about particular brands, with another alleging they received an ad for a medical treatment following a private discussion with a doctor. This is not the first time Siri has been accused of eavesdropping.

Small and medium sized businesses often struggle with data driven decision making do to a lack of advanced analytics tools, especially in African countries where they make up a majority of the workforce. Though many tools exist they are not designed to fit into the ways of working of SMB workers who are mobile first, have limited time to learn new workflows, and for whom social and business are tightly coupled. To address this, the Dukawalla prototype was created. This intelligent assistant bridges the gap between raw business data, and actionable insights by leveraging voice interaction and the power of generative AI. Dukawalla provides an intuitive way for business owners to interact with their data, aiding in informed decision making. This paper examines Dukawalla's deployment across SMBs in Nairobi, focusing on their experiences using this voice based assistant to streamline data collection and provide business insights

Large Language Models (LLMs) are increasingly applied in recommender systems aimed at both individuals and groups. Previously, Group Recommender Systems (GRS) often used social choice-based aggregation strategies to derive a single recommendation based on the preferences of multiple people. In this paper, we investigate under which conditions language models can perform these strategies correctly based on zero-shot learning and analyse whether the formatting of the group scenario in the prompt affects accuracy. We specifically focused on the impact of group complexity (number of users and items), different LLMs, different prompting conditions, including In-Context learning or generating explanations, and the formatting of group preferences. Our results show that performance starts to deteriorate when considering more than 100 ratings. However, not all language models were equally sensitive to growing group complexity. Additionally, we showed that In-Context Learning (ICL) can significantly increase the performance at higher degrees of group complexity, while adding other prompt modifications, specifying domain cues or prompting for explanations, did not impact accuracy. We conclude that future research should include group complexity as a factor in GRS evaluation due to its effect on LLM performance. Furthermore, we showed that formatting the group scenarios differently, such as rating lists per user or per item, affected accuracy. All in all, our study implies that smaller LLMs are capable of generating group recommendations under the right conditions, making the case for using smaller models that require less computing power and costs.

One of the most confounding moments after I got my first Amazon Echo Dot was when its light ring began pulsing yellow, signaling… well, what exactly? Indeed, Echo devices such as the standard Echo, the Echo Dot, the Echo Pop, and the Echo Show can display flashing indicator lights in a range of different colors, and they can be pretty confusing–even aggravating–if you don't know what they mean, or how to make them stop. Luckily, deciphering the flashing lights on your Echo device is simple, and once you know the code, the lights can warn you when something's amiss with your Echo, let you know when you have incoming messages, alert you when Alexa is listening, and more. Let's start with the light that confused me the most when I got my first Echo (the ever-popular Echo Dot). Generally accompanied by a cheerful "bum, bum!" alert tone, the flashing yellow light lets you know when Alexa has a notification for you, or if you missed a reminder.

Clustering bandits have gained significant attention in recommender systems by leveraging collaborative information from neighboring users to better capture target user preferences. However, these methods often lack a clear definition of similar users and face challenges when users with unique preferences lack appropriate neighbors. In such cases, relying on divergent preferences of misidentified neighbors can degrade recommendation quality. To address these limitations, this paper proposes an adaptive Collaborative Combinatorial Bandits algorithm (CoCoB). CoCoB employs an innovative two-sided bandit architecture, applying bandit principles to both the user and item sides. The user-bandit employs an enhanced Bayesian model to explore user similarity, identifying neighbors based on a similarity probability threshold. The item-bandit treats items as arms, generating diverse recommendations informed by the user-bandit's output. CoCoB dynamically adapts, leveraging neighbor preferences when available or focusing solely on the target user otherwise. Regret analysis under a linear contextual bandit setting and experiments on three real-world datasets demonstrate CoCoB's effectiveness, achieving an average 2.4% improvement in F1 score over state-of-the-art methods.

E-commerce platforms generate vast volumes of user feedback, such as star ratings, written reviews, and comments. However, most recommendation engines rely primarily on numerical scores, often overlooking the nuanced opinions embedded in free text. This paper comprehensively reviews sentiment-aware recommendation systems from a natural language processing perspective, covering advancements from 2023 to early 2025. It highlights the benefits of integrating sentiment analysis into e-commerce recommenders to enhance prediction accuracy and explainability through detailed opinion extraction. Our survey categorizes recent work into four main approaches: deep learning classifiers that combine sentiment embeddings with user item interactions, transformer based methods for nuanced feature extraction, graph neural networks that propagate sentiment signals, and conversational recommenders that adapt in real time to user feedback. We summarize model architectures and demonstrate how sentiment flows through recommendation pipelines, impacting dialogue-based suggestions. Key challenges include handling noisy or sarcastic text, dynamic user preferences, and bias mitigation. Finally, we outline research gaps and provide a roadmap for developing smarter, fairer, and more user-centric recommendation tools.