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1. Modeling Users’ Curiosity in Recommender Systems

   https://doi.org/10.1145/3617598  

   Fu, Zhe; Niu, Xi (August 2023, ACM Transactions on Knowledge Discovery from Data)

   Today’s recommender systems are criticized for recommending items that are too obvious to arouse users’ interests. Therefore the research community has advocated some ”beyond accuracy” evaluation metrics such as novelty, diversity, and serendipity with the hope of promoting information discovery and sustaining users’ interests over a long period of time. While bringing in new perspectives, most of these evaluation metrics have not considered individual users’ differences in their capacity to experience those ”beyond accuracy” items. Open-minded users may embrace a wider range of recommendations than conservative users. In this paper, we proposed to use curiosity traits to capture such individual users’ differences. We developed a model to approximate an individual’s curiosity distribution over different stimulus levels. We used an item’s surprise level to estimate the stimulus level and whether such a level is in the range of the user’s appetite for stimulus, calledComfort Zone. We then proposed a recommender system framework that considers both user preference and theirComfort Zonewhere the curiosity is maximally aroused. Our framework differs from a typical recommender system in that it leverages human’sComfort Zonefor stimuli to promote engagement with the system. A series of evaluation experiments have been conducted to show that our framework is able to rank higher the items with not only high ratings but also high curiosity stimulation. The recommendation list generated by our algorithm has higher potential of inspiring user curiosity compared to the state-of-the-art deep learning approaches. The personalization factor for assessing the surprise stimulus levels further helps the recommender model achieve smaller (better) inter-user similarity. 

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2. One Size Does Not Fit All: Modeling Users’ Personal Curiosity in Recommender Systems

   Abbas, Fakhri; Niu, Xi (June 2019, ArXivorg)

   Today’s recommender systems are criticized for recommending items that are too obvious to arouse users’ interest. That is why the recommender systems research community has advocated some ”beyond accuracy” evaluation metrics such as novelty, diversity, coverage, and serendipity with the hope of promoting information discovery and sustain users’ interest over a long period of time. While bringing in new perspectives, most of these evaluation metrics have not considered individual users’ difference: an open-minded user may favor highly novel or diversified recommendations whereas a conservative user’s appetite for novelty or diversity may not be that large. In this paper, we developed a model to approximate an individual’s curiosity distribution over different levels of stimuli guided by the well-known Wundt curve in Psychology. We measured an item’s surprise level to assess the stimulation level and whether it is in the range of the user’s appetite for stimulus. We then proposed a recommendation system framework that considers both user preference and appetite for stimulus where the curiosity is maximally aroused. Our framework differs from a typical recommender system in that it leverages human’s curiosity to promote intrinsic interest with the system. A series of evaluation experiments have been conducted to show that our framework is able to rank higher the items with not only high ratings but also high response likelihood. The recommendation list generated by our algorithm has higher potential of inspiring user curiosity compared to traditional approaches. The personalization factor for assessing the stimulus (surprise) strength further helps the recommender achieve smaller (better) inter-user similarity. 

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3. Computational Surprise, Perceptual Surprise, and Personal Background in Text Understanding

   https://doi.org/10.1145/3295750.3298963  

   Niu, Xi; Abbas, Fakhri (March 2019, Proceedings of 2019 ACM SIGIR International Conference on the Theory of Information Retrieval)

   The concept of surprise has special significance in information retrieval in attracting user attention and arousing curiosity. In this paper, we introduced two computational measures of calculating the amount of surprise contained in a piece of text, and validated with the perceived surprise by users with different background knowledge expertise. We utilized a crowdsourcing approach and a lab-based user study to reach a large amount of users. The implication could be used to propose or refine future computational approaches to better predict human feeling of surprise triggered by reading a body of text. 

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4. Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives

   https://doi.org/10.1145/3605145  

   Fu, Zhe; Niu, Xi; Maher, Mary Lou (January 2024, ACM Computing Surveys)

   Serendipitous recommendations have emerged as a compelling approach to deliver users with unexpected yet valuable information, contributing to heightened user satisfaction and engagement. This survey presents an investigation of the most recent research in serendipity recommenders, with a specific emphasis on deep learning recommendation models. We categorize these models into three types, distinguishing their integration of the serendipity objective across distinct stages: pre-processing, in-processing, and post-processing. Additionally, we provide a review and summary of the serendipity definition, available ground truth datasets, and evaluation experiments employed in the field. We propose three promising avenues for future exploration: (1) leveraging user reviews to identify and explore serendipity, (2) employing reinforcement learning to construct a model for discerning appropriate timing for serendipitous recommendations, and (3) utilizing cross-domain learning to enhance serendipitous recommendations. With this review, we aim to cultivate a deeper understanding of serendipity in recommender systems and inspire further advancements in this domain. 

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5. Leveraging Generative AI to Improve Comprehensibility in Social Recommender Systems

   https://doi.org/10.1145/3708319.3733696  

   Ashaduzzaman, Md; Tsai, Chun-Hua (June 2025, ACM)

   Generative AI, particularly Large Language Models (LLMs), has revolutionized human-computer interaction by enabling the generation of nuanced, human-like text. This presents new opportunities, especially in enhancing explainability for AI systems like recommender systems, a crucial factor for fostering user trust and engagement. LLM-powered AI-Chatbots can be leveraged to provide personalized explanations for recommendations. Although users often find these chatbot explanations helpful, they may not fully comprehend the content. Our research focuses on assessing how well users comprehend these explanations and identifying gaps in understanding. We also explore the key behavioral differences between users who effectively understand AI-generated explanations and those who do not. We designed a three-phase user study with 17 participants to explore these dynamics. The findings indicate that the clarity and usefulness of the explanations are contingent on the user asking relevant follow-up questions and having a motivation to learn. Comprehension also varies significantly based on users’ educational backgrounds. 

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