Large Language Models (LLMs) have greatly advanced knowledge graph question answering (KGQA), yet existing systems are typically optimized for returning highly relevant but predictable answers. A missing yet desired capacity is to exploit LLMs to suggest surprise and novel ("serendipitious") answers. In this paper, we formally define the serendipity-aware KGQA task and propose the SerenQA framework to evaluate LLMs' ability to uncover unexpected insights in scientific KGQA tasks. SerenQA includes a rigorous serendipity metric based on relevance, novelty, and surprise, along with an expert-annotated benchmark derived from the Clinical Knowledge Graph, focused on drug repurposing. Additionally, it features a structured evaluation pipeline encompassing three subtasks: knowledge retrieval, subgraph reasoning, and serendipity exploration. Our experiments reveal that while state-of-the-art LLMs perform well on retrieval, they still struggle to identify genuinely surprising and valuable discoveries, underscoring a significant room for future improvements. Our curated resources and extended version are released at: https://cwru-db-group.github.io/serenQA.

This paper introduces "Synthetic Interlocutors" for ethnographic research. Synthetic Interlocutors are chatbots ingested with ethnographic textual material (interviews and observations) by using Retrieval Augmented Generation (RAG). We integrated an open-source large language model with ethnographic data from three projects to explore two questions: Can RAG digest ethnographic material and act as ethnographic interlocutor? And, if so, can Synthetic Interlocutors prolong encounters with the field and extend our analysis? Through reflections on the process of building our Synthetic Interlocutors and an experimental collaborative workshop, we suggest that RAG can digest ethnographic materials, and it might lead to prolonged, yet uneasy ethnographic encounters that allowed us to partially recreate and re-visit fieldwork interactions while facilitating opportunities for novel analytic insights. Synthetic Interlocutors can produce collaborative, ambiguous and serendipitous moments.

Mental health experts, regulators, and many internet users themselves have called out the damage that social media can do to mental health. Must it addict, inflame, and depress us? A new social network called Maven aims to offer a healthier alternative, inspired by one scientist's work in artificial intelligence. The platform eschews likes and follows in favor of letting pure chance play more of a role in what appears in users' feeds. Maven's lead investor is Twitter cofounder and former CEO Ev Williams, who also founded Medium.

Traditional recommendation proposals, including content-based and collaborative filtering, usually focus on similarity between items or users. Existing approaches lack ways of introducing unexpectedness into recommendations, prioritizing globally popular items over exposing users to unforeseen items. This investigation aims to design and evaluate a novel layer on top of recommender systems suited to incorporate relational information and suggest items with a user-defined degree of surprise. We propose a Knowledge Graph (KG) based recommender system by encoding user interactions on item catalogs. Our study explores whether network-level metrics on KGs can influence the degree of surprise in recommendations. We hypothesize that surprisingness correlates with certain network metrics, treating user profiles as subgraphs within a larger catalog KG. The achieved solution reranks recommendations based on their impact on structural graph metrics. Our research contributes to optimizing recommendations to reflect the metrics. We experimentally evaluate our approach on two datasets of LastFM listening histories and synthetic Netflix viewing profiles. We find that reranking items based on complex network metrics leads to a more unexpected and surprising composition of recommendation lists.

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.

A restaurant dinner may become a memorable experience due to an unexpected aspect enjoyed by the customer, such as an origami-making station in the waiting area. If aspects that are atypical for a restaurant experience were known in advance, they could be leveraged to make recommendations that have the potential to engender serendipitous experiences, further increasing user satisfaction. Although relatively rare, whenever encountered, atypical aspects often end up being mentioned in reviews due to their memorable quality. Correspondingly, in this paper we introduce the task of detecting atypical aspects in customer reviews. To facilitate the development of extraction models, we manually annotate benchmark datasets of reviews in three domains - restaurants, hotels, and hair salons, which we use to evaluate a number of language models, ranging from fine-tuning the instruction-based text-to-text transformer Flan-T5 to zero-shot and few-shot prompting of GPT-3.5.

Recently we are witnessing a major shift in the process of generating images. The recent influx and growth of machine learning and artificial intelligence rises questions about the way in which creative processes evolve and develop through technology. Systems like DALL-E, DALL-E 2 and Midjourney are AI programs trained to generate images from text descriptions using a dataset of text-image pairs. The diverse set of capabilities includes creating anthropomorphized versions of animals and objects, combining unrelated concepts in plausible ways, and applying transformations to existing images. DALL-E and similar systems are able to create plausible images for a great variety of sentences that explore the compositional structure of language.

Music is liquid architecture; architecture is frozen music.--Attributed to Goethe But Missa solemnis 2.0, a collaboration between pioneering media artist and director Refik Anadol and The Philadelphia Orchestra (April 7, 9, 10, supported by The Pew Center for Arts & Heritage), brings Goethe's pithy saying to stunning visual and sonic life in ways the German literary giant never could have imagined. Beethoven completed his Missa solemnis in 1823. Despite being regarded as one of his most stunning musical creations, the piece is rarely performed. The composer's partner in this century-spanning project, Refik Anadol, was born in Istanbul. In 2008, while still an undergrad there, he presented his first digital art installation.

We propose a method to generate explainable recommendation rules on cross-domain problems. Our two main contributions are: i) using relational learning to generate the rules which are able to explain clearly why the items were recommended to the particular user, ii) using the user's preferences of items on different domains and item attributes to generate novel or unexpected recommendations for the user. To illustrate that our method is indeed feasible and applicable, we conducted experiments on music and movie domains.

In an example of biomimicry, cicada wings could inspire bacteria-resistent materials. Knowledge transfer across domains leads to significant breakthroughs in science and technology. For example, through biomimicry, innovators get inspiration from nature/biology to solve complex engineering problems. An exciting example of biomimicry is the recent creation of artificial materials that imitate the surface of cicada's wings and gecko's skin, which have antibacterial properties due to their physical structure. These type of materials could be used in hospitals for surfaces that get easily contaminated with bacteria and help drastically reduce the number of hospital infections, a leading cause of health complications during hospitalization.