User behavior data in recommender systems are driven by the complex interactions of many latent factors behind the users' decision making processes.

Explicitly characterizing an object-level relationship while distilling spatial relationships are currently absent but crucial to this task. Mimicking humans, who sequentially traverse spatial relationship words and objects with a first-person view to locate their target, we propose a novel spatial relationship induced (SIRI) network.

There are pairs for which the odds are really, well, coinflip! These are not criticisms, but differences to be aware of. Q2: Please summarize your review in 1-2 sentences A beautifully written paper that shows that the users are "learnable" after around log(number of user types) + log(number of items) rounds of recommendations, given a simple algorithm. The result proof relies on a number of conditions to be met, which are essentially a "simplification of the real world", and tells us that structured (joint) exploration of users is a requirement.

Neural Information Processing Systems http://nips.cc/

We develop a universal, parameter-free, and computationally efficient algorithm that starts with hierarchical graph clustering and then iteratively refines estimates both on graph clustering and matrix ratings.

The paper asserts that emulating empathy in human-robot interaction is a key component to achieve satisfying social, trustworthy, and ethical robot interaction with older people. Following comments from older adult study participants, the paper identifies a gap. Despite the acceptance of robot care scenarios, participants expressed the poor quality of the social aspect. Current human-robot designs, to a certain extent, neglect to include empathy as a theorized design pathway. Using rhetorical theory, this paper defines the socio-cultural expectations for convincing empathetic relationships. It analyzes and then summarizes how society understands, values, and negotiates empathic interaction between human companions in discursive exchanges, wherein empathy acts as a societal value system. Using two public research collections on robots, with one geared specifically to gerontechnology for older people, it substantiates the lack of attention to empathy in public materials produced by robot companies. This paper contends that using an empathetic care vocabulary as a design pathway is a productive underlying foundation for designing humanoid social robots that aim to support older people's goals of aging-in-place. It argues that the integration of affective AI into the sociotechnical assemblages of human-socially assistive robot interaction ought to be scrutinized to ensure it is based on genuine cultural values involving empathetic qualities.

Effective conversational agents like large language models (LLMs) must personalize their interactions to adapt to user preferences, personalities, and attributes across diverse domains like education and healthcare. Current methods like Reinforcement Learning from Human Feedback (RLHF), often prioritize helpfulness and safety but fall short in fostering truly empathetic, adaptive, and personalized dialogues. Existing personalization approaches typically rely on extensive user history, limiting their effectiveness for new or context-limited users. To address these limitations, we propose leveraging a user model to incorporate a curiosity-based intrinsic reward into multi-turn RLHF. This novel reward mechanism encourages the LLM agent to actively infer user traits by optimizing conversations to improve its user model's accuracy. Consequently, the agent delivers more personalized interactions by learning more about the user. We demonstrate our method's effectiveness in two distinct domains: significantly improving personalization performance in a conversational recommendation task, and personalizing conversations for different learning styles in an educational setting. We show improved generalization capabilities compared to traditional multi-turn RLHF, all while maintaining conversation quality. Our method offers a promising solution for creating more personalized, adaptive, and engaging conversational agents.

Robo-advisors (RAs) are cost-effective, bias-resistant alternatives to human financial advisors, yet adoption remains limited. While prior research has examined user interactions with RAs, less is known about how individuals interpret RA roles and integrate their advice into decision-making. To address this gap, this study employs a multiphase mixed methods design integrating a behavioral experiment (N = 334), thematic analysis, and follow-up quantitative testing. Findings suggest that people tend to rely on RAs, with reliance shaped by information about RA performance and the framing of advice as gains or losses. Thematic analysis reveals three RA roles in decision-making and four user types, each reflecting distinct patterns of advice integration. In addition, a 2 x 2 typology categorizes antecedents of acceptance into enablers and inhibitors at both the individual and algorithmic levels. By combining behavioral, interpretive, and confirmatory evidence, this study advances understanding of human-RA collaboration and provides actionable insights for designing more trustworthy and adaptive RA systems.