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1. 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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2. 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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3. Wisdom of Crowds and Fine-Grained Learning for Serendipity Recommendations

   https://doi.org/10.1145/3539618.3591787  

   Fu, Zhe; Niu, Xi; Yu, Li (July 2023, ACM)

   Serendipity is a notion that means an unexpected but valuable discovery. Due to its elusive and subjective nature, serendipity is difficult to study even with today's advances in machine learning and deep learning techniques. Both ground truth data collecting and model developing are the open research questions. This paper addresses both the data and the model challenges for identifying serendipity in recommender systems. For the ground truth data collecting, it proposes a new and scalable approach by using both user generated reviews and a crowd sourcing method. The result is a large-scale ground truth data on serendipity. For model developing, it designed a self-enhanced module to learn the fine-grained facets of serendipity in order to mitigate the inherent data sparsity problem in any serendipity ground truth dataset. The self-enhanced module is general enough to be applied with many base deep learning models for serendipity. A series of experiments have been conducted. As the result, a base deep learning model trained on our collected ground truth data, as well as with the help of the self-enhanced module, outperforms the state-of-the-art baseline models in predicting serendipity. 

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4. Deep Hierarchical Knowledge Tracing

   Wang, T.; Ma, F.; Gao, J. (January 2019, Proceedings of the 12th International Conference on Educational Data Mining)

   Knowledge tracing is an essential and challenging task in intelligent tutoring systems, whose goal is to estimate students’ knowledge state based on their responses to questions. Although many models for knowledge tracing task are developed, most of them depend on either concepts or items as input and ignore the hierarchical structure of items, which provides valuable information for the prediction of student learning results. In this paper, we propose a novel deep hierarchical knowledge tracing (DHKT) model exploiting the hierarchical structure of items. In the proposed DHKT model, the hierarchical relations between concepts and items are modeled by the hinge loss on the inner product between the learned concept embeddings and item embeddings. Then the learned embeddings are fed into a neural network to model the learning process of students, which is used to make predictions. The prediction loss and the hinge loss are minimized simultaneously during training process. 

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5. The Art of Asking: Prompting Large Language Models for Serendipity Recommendations

   https://doi.org/10.1145/3664190.3672521  

   Fu, Zhe; Niu, Xi (August 2024, ACM ICTIR '24: Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval)

   Serendipity means an unexpected but valuable discovery. Its elusive nature makes it susceptible to modeling. In this paper, we address the challenge of modeling serendipity in recommender systems using Large Language Models (LLMs), a recent breakthrough in AI technologies. We leveraged LLMs' prompting mechanisms to convert a problem of serendipity recommendations into a problem of formulating a prompt to elicit serendipity recommendations. The formulated prompt is called SerenPrompt. We designed three types of SerenPrompt: discrete with natural words, continuous with trainable tokens, and hybrid that combines the previous two types. In the meanwhile, for each type of SerenPrompt, we also designed two styles: direct and indirect, to investigate whether it is feasible to directly ask an LLM a question on whether an item is a serendipity, or we should breakdown the question into several sub-questions. Extensive experiments have demonstrated the effectiveness of SerenPrompt in generating serendipity recommendations, compared to the state-of-the-art models. The combination of the hybrid type and the indirect style achieves the best performance, with relatively low sacrifice to computational efficiency. The results demonstrate that natural words and virtual tokens, as building blocks of LLM prompts, each have their own advantages. The better performance of the indirect style speaks to the effectiveness of decomposing the direct question on serendipity. 

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