In community question answering (cQA) platforms like Stack Overflow, related question retrieval is recognized as a fundamental task that allows users to retrieve related questions to answer user queries automatically. Although many traditional approaches have been proposed for investigating this research field, they mostly rely on static approaches and neglect the interaction property. We argue that the conversational way can well distinguish the fine-grained representations of questions and has great potential to improve the performance of question retrieval. In this paper, we propose a related question retrieval model through conversations, called TeCQR, to locate related questions in cQA. Specifically, we build conversations by utilizing tag-enhanced clarifying questions. In addition, we design a noise tolerance model that evaluates the semantic similarity between questions and tags, enabling the model to effectively handle noisy feedback. Moreover, the tag-enhanced two-stage offline training is proposed to fully exploit the mutual relationships among user queries, questions, and tags to learn their fine-grained representations. Based on the learned representations and contextual conversations, TeCQR incorporates conversational feedback by learning to ask tag-enhanced clarifying questions to retrieve related questions more effectively. Experimental results demonstrate that our model significantly outperforms state-of-the-art baselines.

Conversational Recommender Systems (CRSs) aim to provide personalized recommendations by interacting with users through conversations. Most existing studies of CRS focus on extracting user preferences from conversational contexts. However, due to the short and sparse nature of conversational contexts, it is difficult to fully capture user preferences by conversational contexts only. We argue that multi-modal semantic information can enrich user preference expressions from diverse dimensions (e.g., a user preference for a certain movie may stem from its magnificent visual effects and compelling storyline). In this paper, we propose a multi-modal semantic graph prompt learning framework for CRS, named MSCRS. First, we extract textual and image features of items mentioned in the conversational contexts. Second, we capture higher-order semantic associations within different semantic modalities (collaborative, textual, and image) by constructing modality-specific graph structures. Finally, we propose an innovative integration of multi-modal semantic graphs with prompt learning, harnessing the power of large language models to comprehensively explore high-dimensional semantic relationships. Experimental results demonstrate that our proposed method significantly improves accuracy in item recommendation, as well as generates more natural and contextually relevant content in response generation. Code and extended multi-modal CRS datasets are available at https://github.com/BIAOBIAO12138/MSCRS-main.

Recent studies tend to employ Contrastive Learning (CL) methods to facilitate model training by extracting self-supervised signals to mitigate data sparsity . However, existing CL-based recommendation methods have not fully exploited the rich semantic information present in multi-modal data. To address these limitations, we propose a new CL-based recommendation framework named Multi-level Cross-modal Contrastive Learning (MCCL), which aims to construct multi-level contrastive learning to fully exploit the intra- and inter-modal semantic information in a self-supervised manner. Specifically, we innovatively consider user interaction and semantic review as two distinct semantic modalities, and devise two modal-specific contrastive learning strategies to enhance intra-modal learning. Furthermore, we leverage the semantic consistency between modalities to construct a multi-level cross-modal contrastive learning framework. Finally, a multi-task learning method is employed for collaborative optimization across multiple tasks. We verify the efficacy of MCCL via comprehensive experiments on three real-world datasets. MCCL achieves a significant performance improvement over the state-of-the-art baseline models .