Serendipity means unexpected discoveries that are valuable, with positive outcomes ranging from personal benefits to scientific breakthroughs. This study proposes a cross-domain recommendation model, called
This content will become publicly available on January 31, 2025
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.
more » « less- Award ID(s):
- 1910696
- PAR ID:
- 10467126
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Computing Surveys
- Volume:
- 56
- Issue:
- 1
- ISSN:
- 0360-0300
- Page Range / eLocation ID:
- 1 to 26
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
SerenCDR , to model serendipity.SerenCDR leverages the knowledge beyond one domain as well as mitigates the inherent data sparsity problem in serendipity recommendations. The novelty ofSerenCDR lies in the fact that it is the first deep learning-based cross-domain model for a serendipity task. More importantly, it does not rely on any overlapping users or overlapping items across different domains, which especially fits for the task of recommending serendipity, because serendipity in a single domain tends to be sparse; finding overlapping users or overlapping items in other domains are nearly impossible. To train and testSerenCDR , we have collected a two-domain ground truth dataset on serendipity, calledSerenCDRLens . In addition, since we found that serendipity is sparse inSerenCDRLens , we designed an auxiliary loss function to supplement the main loss function to enhance serendipity learning. Through a series of experiments, we have harvested positive performance in recommending serendipity, empowering users with increased chances of bumping into unexpected but valuable discoveries. -
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.more » « less
-
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.more » « less
-
A learning strategy analysis was performed on the Emotive Virtual Patient System, an augmented reality platform that teaches medical students doctor-patient communication skills. The Emotive Virtual Patient System is a complex mixed reality platform that includes both virtual and human peers/instructors who use natural language processing to provide feedback and dialog modeling as a means to improve patient communication learning outcomes. The learning strategy analysis (i.e., system learning strategy/component review, literature review, and heuristic evaluation of best practices) was conducted on the early system plans to determine its potential in supporting student learning and to provide shortand-long-term design considerations. The analysis identified three major categories for potential consideration: verbal interactions, user groups/system objective monitoring, and security. Specific recommendations were given for each of these areas, as supported by the literature.more » « less
-
more » « less
Abstract Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug–drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, natural language processing based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.
