Search result diversification focuses on providing relevant and diverse documents covering different users’ intents. Intuitively, training an effective and stable search result diversification model needs a large amount of training data. Unfortunately, annotating such data that encompass real users’ search intents is expensive and time-consuming, and most existing models are trained with limited training data, which might lead to unsatisfactory ranking results. Given that Wikipedia contains massive amounts of rigorous editorial and well-structured data, in this article, we propose a pre-training framework leveraging the large-scale Wikipedia data to build weak-supervised signals. Specifically, we introduce four strategies to extract paired supervised signals reflecting the subtopic coverage information from Wikipedia. We also propose a subtopic-disentangled negative sampling strategy to sample hard negative samples and enhance the model’s ability to identify subtle subtopic differences. Four auxiliary tasks are devised to pre-train the Transformer model, which is further adopted as the representation generation model in the downstream diversified ranking. Experimental results demonstrate that our pre-trained model can significantly improve the performance of several existing models, which confirms the effectiveness and scalability of pre-training for search result diversification.

The diversified recommendation aims to satisfy a user’s different preferences and hence alleviates the information cocoon problem. Previous methods focus on increasing the sample probability of interacted items in the long-tail category. However, these methods are limited by the scope of the historical interactions of a single user and confront the challenge of inadequate diversity of past interactions and unpredictable potential diverse preferences. Drawing from social cognitive theory, observational learning ability allows humans to imitate others’ behaviors when their experience is insufficient. Inspired by it, in this article, we apply the idea of observational learning to the diversified recommendation and introduce a social Cog nitive Theory Enhanced D iversified R ecommendation ( Cog4DR ) model. Specifically, we design a three-step observational learning pipeline, including attention, purification, and retention, corresponding to the three essential stages of observational learning. The pipeline enables the current user to observe other users who have similar tastes but also engage with unique categories, therefore exploring potential diverse preferences and achieving dual improvements in accuracy and diversity. Experimental results indicate that Cog4DR outperforms all previous approaches, demonstrating the effectiveness of imitating other users’ behaviors for diversified recommendations.

Search result diversification plays a crucial role in addressing query ambiguity and multi-faceted information needs by reducing redundancy across documents. While previous supervised approaches can achieve superior performance, they require costly, large-scale annotated data. In contrast, unsupervised methods are more flexible and training-free but rely on manually designed ranking functions, often leading to suboptimal performance. Inspired by how humans explore diverse information during real-world searching, we propose a diversified search agent DIVAgent to combine the advantages of supervised and unsupervised methods. DIVAgent introduces LLMs as the ''brain'' to reason over complex and diverse search results and delineate human cognitive processes into a workflow tailored for search result diversification. Our search agent first identifies potential user intents and then analyzes the alignment of each document to the intents via an intent-aware module. To guide the generation of diversified document rankings, we design an intent-guided ranker that explicitly links documents to their dominant intents while performing greedy document selection. Experimental results demonstrate that DIVAgent significantly outperforms existing unsupervised baselines and achieves competitive performance with supervised models, highlighting the promise of LLMs for diversified ranking in realistic search scenarios.

In modern information retrieval (IR), going beyond accuracy is crucial for maintaining a healthy ecosystem, particularly in meeting fairness and diversity requirements. To address these needs, various datasets, algorithms, and evaluation methods have been developed. These algorithms are often tested with different metrics, datasets, and experimental settings, making comparisons inconsistent and challenging. Consequently, there is an urgent need for a comprehensive IR toolkit, enabling standardized assessments of fairness- and diversity-aware algorithms across IR tasks. To address these issues, we introduce an open-source standardized toolkit called FairDiverse. First, FairDiverse provides a comprehensive framework for incorporating fairness- and diversity-aware approaches, including pre-processing, in-processing, and post-processing methods, into different pipeline stages of IR. Second, FairDiverse enables the evaluation of 29 fairness, and diversity algorithms across 16 base models for two fundamental IR tasks-search and recommendation-facilitating the establishment of a comprehensive benchmark. Finally, FairDiverse is highly extensible, offering multiple APIs to enable IR researchers to quickly develop their own fairness- and diversity-aware IR models, and allows for fair comparisons with existing baselines. The project is open-sourced on GitHub:~ https://github.com/XuChen0427/FairDiverse.

The diversification of product search aims to offer diverse products to satisfy different user intents. Existing diversified product search approaches mainly relied on datasets sourced from online platforms. However, these datasets often present challenges due to their restricted public access and the absence of manually labeled user intents. Such limitations may lead to irreproducible experimental results and unreliable conclusions, restricting the development of this field. To address these problems, this paper introduces a novel dataset JDivPS for diversified product search. To the best of our knowledge, JDivPS is the first publicly accessible dataset with human-annotated user intents. The dataset is collected from JD.com, a major Chinese e-commerce platform. It includes 10,000 queries, around 1,680,000 unique products, and an average of 10 human-labeled user intents for each query. We have extensively evaluated several diversified ranking models using the JDivPS dataset. The results of these models are recorded and presented, serving as a valuable benchmark for future research. More details about the dataset can be found in https://github.com/DengZhirui/JDivPS.

Search result diversification aims to provide a diversified document ranking list so as to cover as many intents as possible and satisfy the various information needs of different users. Existing approaches usually represented documents by pretrained embeddings (such as doc2vec and Glove). These document representations cannot adequately represent the document's content and are hard to capture the intrinsic user's intent coverage of the given query. Moreover, the limited number of labeled data for search result diversification exacerbates the difficulty of obtaining more efficient document representations. To alleviate these problems and learn more effective document representations, we propose a Contrastive Learning framework for search result DIVersification (CL4DIV). Specifically, we design three contrastive learning tasks from the perspective of subtopics, documents, and candidate document sequences, which correspond to three essential elements in search result diversification. These training tasks are employed to pretrain the document encoder and the document sequence encoder, which are used in the diversified ranking model. Experimental results show that øurs significantly outperforms all existing diversification models. Further analysis demonstrates that our method has wide applicability and can also be used to improve several existing methods.

Search result diversification plays a crucial role in improving users’ search experience by providing users with documents covering more subtopics. Previous studies have made great progress in leveraging inter-document interactions to measure the similarity among documents. However, different parts of the document may embody different subtopics and existing models ignore the subtle similarities and differences of content within each document. In this article, we propose a hierarchical attention framework to combine intra-document interactions with inter-document interactions in a complementary manner in order to conduct multi-grained document modeling. Specifically, we separate the document into passages to model the document content from multi-grained perspectives. Then, we design stacked interaction blocks to conduct inter-document and intra-document interactions. Moreover, to measure the subtopic coverage of each document more accurately, we propose a passage-aware document-subtopic interaction to perform fine-grained document-subtopic interaction. Experimental results demonstrate that our model achieves state-of-the-art performance compared with existing methods.