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1. Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events

   https://doi.org/10.1111/tgis.13282  

   Tian, Yuanyuan; Li, Wenwen; Hu, Lei; Chen, Xiao; Brook, Michael; Brubaker, Michael; Zhang, Fan; Liljedahl, Anna_K (December 2024, Transactions in GIS)

   ABSTRACT Retrieval and recommendation are two essential tasks in modern search tools. This paper introduces a novel retrieval‐reranking framework leveraging large language models to enhance the spatiotemporal and semantic associated mining and recommendation of relevant, unusual climate and environmental events described in news articles and web posts. This framework uses advanced natural language processing techniques to address the limitations of traditional manual curation methods in terms of high labor costs and lack of scalability. Specifically, we explore an optimized solution to employ cutting‐edge embedding models for semantically analyzing spatiotemporal events (news) and propose a Geo‐Time Re‐ranking strategy that integrates multi‐faceted criteria including spatial proximity, temporal association, semantic similarity, and category‐instructed similarity to rank and identify similar spatiotemporal events. We apply the proposed framework to a dataset of four thousand local environmental observer network events, achieving top performance on recommending similar events among multiple cutting‐edge dense retrieval models. The search and recommendation pipeline can be applied to a wide range of similar data search tasks dealing with geospatial and temporal data. We hope that by linking relevant events, we can better aid the general public to gain enhanced understanding on climate change and its impact on different communities. 

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2. Large Language Model Augmented Narrative Driven Recommendations

   https://doi.org/10.1145/3604915  

   Mysore, Sheshera; McCallum, Andrew; Zamani, Hamed (September 2023, ACM)

   Zhang, Jie; Chen, Li; Berkovsky, Shlomo; Zhang, Min; Noia, Tommaso di; Basilico, Justin; Pizzato, Luiz; Song, Yang (Ed.)

   Narrative-driven recommendation (NDR) presents an information access problem where users solicit recommendations with verbose descriptions of their preferences and context, for example, travelers soliciting recommendations for points of interest while describing their likes/dislikes and travel circumstances. These requests are increasingly important with the rise of natural language-based conversational interfaces for search and recommendation systems. However, NDR lacks abundant training data for models, and current platforms commonly do not support these requests. Fortunately, classical user-item interaction datasets contain rich textual data, e.g., reviews, which often describe user preferences and context – this may be used to bootstrap training for NDR models. In this work, we explore using large language models (LLMs) for data augmentation to train NDR models. We use LLMs for authoring synthetic narrative queries from user-item interactions with few-shot prompting and train retrieval models for NDR on synthetic queries and user-item interaction data. Our experiments demonstrate that this is an effective strategy for training small-parameter retrieval models that outperform other retrieval and LLM baselines for narrative-driven recommendation. 

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3. Large Language Model Augmented Narrative Driven Recommendations

   https://doi.org/10.1145/3604915.3608829  

   Mysore, Sheshera; Mccallum, Andrew; Zamani, Hamed (September 2023, ACM)

   Narrative-driven recommendation (NDR) presents an information access problem where users solicit recommendations with verbose descriptions of their preferences and context, for example, travelers soliciting recommendations for points of interest while describing their likes/dislikes and travel circumstances. These requests are increasingly important with the rise of natural language-based conversational interfaces for search and recommendation systems. However, NDR lacks abundant training data for models, and current platforms commonly do not support these requests. Fortunately, classical user-item interaction datasets contain rich textual data, e.g., reviews, which often describe user preferences and context – this may be used to bootstrap training for NDR models. In this work, we explore using large language models (LLMs) for data augmentation to train NDR models. We use LLMs for authoring synthetic narrative queries from user-item interactions with few-shot prompting and train retrieval models for NDR on synthetic queries and user-item interaction data. Our experiments demonstrate that this is an effective strategy for training small-parameter retrieval models that outperform other retrieval and LLM baselines for narrative-driven recommendation. 

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4. Learning a Joint Search and Recommendation Model from User-Item Interactions

   https://doi.org/10.1145/3336191.3371818  

   Zamani, Hamed; Croft, W. Bruce (February 2020, Proceedings of the 13th International Conference on Web Search and Data Mining)

   Existing learning to rank models for information retrieval are trained based on explicit or implicit query-document relevance information. In this paper, we study the task of learning a retrieval model based on user-item interactions. Our model has potential applications to the systems with rich user-item interaction data, such as browsing and recommendation, in which having an accurate search engine is desired. This includes media streaming services and e-commerce websites among others. Inspired by the neural approaches to collaborative filtering and the language modeling approaches to information retrieval, our model is jointly optimized to predict user-item interactions and reconstruct the item textual descriptions. In more details, our model learns user and item representations such that they can accurately predict future user-item interactions, while generating an effective unigram language model for each item. Our experiments on four diverse datasets in the context of movie and product search and recommendation demonstrate that our model substantially outperforms competitive retrieval baselines, in addition to providing comparable performance to state-of-the-art hybrid recommendation models. 

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5. Anderson and Krathwohl's Two-Dimensional Taxonomy Applied to Task Creation and Learning Assessment

   https://doi.org/10.1145/3341981.3344226  

   Urgo, K.; Arguello, J.; Capra, R. (October 2019, Proceedings of the 5th ACM SIGIR International Conference on the Theory of Information Retrieval)

   Search tasks play an important role in the study and development of interactive information retrieval (IIR) systems. Prior work has examined how search tasks vary along dimensions such as the task’s main activity, end goal, structure, and complexity. Recently, researchers have been exploring task complexity from the perspective of cognitive complexity—related to the types (and variety) of mental activities required by the task. Anderson & Krathwohl’s two-dimensional taxonomy of learning has been a commonly used framework for investigating tasks from the perspective of cognitive complexity [1]. A&K’s 2D taxonomy involves a cognitive process dimension and an orthogonal knowledge dimension. Prior IIR research has successfully leveraged the cognitive process dimension of this 2D taxonomy to develop search tasks and investigate their effects on searchers’ needs, perceptions, and behaviors. However, the knowledge dimension of the taxonomy has been largely ignored. In this conceptual paper, we argue that future IIR research should consider both dimensions of A&K’s taxonomy. Specifically, we discuss related work, present details on both dimensions of A&K’s taxonomy, and explain how to use the taxonomy to develop search tasks and learning assessment materials. Additionally, we discuss how considering both dimensions of A&K’s taxonomy has important implications for future IIR research. 

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