Conversational recommender systems (CRSs) are able to elicit user preferences through multi-turn dialogues. They typically incorporate external knowledge and pre-trained language models to capture the dialogue context. Most CRS approaches, trained on benchmark datasets, assume that the standard items and responses in these benchmarks are optimal. However, they overlook that users may express negative emotions with the standard items and may not feel emotionally engaged by the standard responses. This issue leads to a tendency to replicate the logic of recommenders in the dataset instead of aligning with user needs. To remedy this misalignment, we introduce empathy within a CRS. With empathy we refer to a system’s ability to capture and express emotions. We propose an empathetic conversational recommender (ECR) framework.

Distributed clustering is emerging along with the advent of the era of big data. However, most existing established distributed clustering methods focus on problems caused by a large amount of data rather than caused by the large dimension of data. Consequently, they suffer the “curse” of dimensionality (e.g., poor performance and heavy network overhead) when high-dimensional (HD) data are clustered. In this article, we propose a distributed algorithm, referred to as Local Density Subspace Distributed Clustering (LDSDC) algorithm, to cluster large-scale HD data, motivated by the idea that a local dense region of a HD dataset is usually distributed in a low-dimensional (LD) subspace. LDSDC follows a local-global-local processing structure, including grouping of local dense regions (atom clusters) followed by subspace Gaussian model (SGM) fitting (flexible and scalable to data dimension) at each sub-site, merging of atom clusters at every sub-site according to the merging result broadcast from the global site. Moreover, we propose a fast method to estimate the parameters of SGM for HD data, together with its convergence proof. We evaluate LDSDC on both synthetic and real datasets and compare it with four state-of-the-art methods. The experimental results demonstrate that the proposed LDSDC yields best overall performance.

Density-based clustering algorithms have the distinctive advantage of discovering arbitrarily shaped clusters, but they usually require a procedure to compute the distance between every pair of data points, and this procedure is prohibitive for large datasets since it has quadratic computation complexity. In this paper, we propose a new distributed clustering algorithm, named REstore MOdel with Local Density estimation (REMOLD). Firstly, REMODL applies a balanced partitioning method to evenly divide an large dataset based on Local Sensitive Hashing (LSH). Then, it locally clusters each partition of the dataset, and uses a Gaussian model to represent each local cluster based on the observation that the density distribution of each local cluster shares similar shape with Gaussian distribution. Finally, these models are aggregated on a server where REMOLD restores global clusters based on these local Gaussian models. More specifically, model connection, which measures the density connectivity between two models, are defined to merge local models with an optimized procedure. In this aggregation, REMOLD requires low cost of network transmission for local Gaussian models, since the number of Gaussian models is often less than that of core objects for each partition. We evaluate REMOLD on three synthetic datasets and three real-world datasets on Spark, and the experiment results demonstrate that REMOLD is efficient and effective to find out clusters with complex shapes and it outperforms the established methods.