In the recommender system field, diversity as the measure of recommendation quality has gained much attention recently. However, many pieces of research have shown that it has a trade-off relation with predictive performance. To improve recommendation diversity and predictive performance in multi-criteria recommender systems, we propose a clustering-based parallel tensor factorization (ClustPTF). In the ClustPTF, sentiment analysis alleviates model sparsity, and the K-means clustering considering rating behaviors groups similar user preferences into sub-models and leads to improve recommendation diversity. The sub-models are then factorized in parallel to predict ratings in near real-time. With one dataset gathered from TripAdvisor, experiments showed that the ClustPTF considerably improve recommendation diversity (13.44x of a conventional tensor factorization (TF₀)) and response time (23.13x of the TF₀). Even its predictive performance is superior to the TF₀ (41.06% improvement in MAE). Furthermore, the ClustPTF outperformed recent techniques in recommendation diversity and predictive performance (i.e., MAE and precision).

在推荐系统领域，多样性作为推荐质量的度量标准最近得到了广泛的关注。但是，许多研究表明，它与预测性能之间存在取舍关系。为了提高多标准推荐系统中的推荐多样性和预测性能，我们提出了一种基于聚类的并行张量因子分解（ClustPTF）。在ClustPTF中，情感分析减轻了模型的稀疏性，考虑评分行为的K-means聚类将相似的用户偏好分组为子模型，从而改善了建议的多样性。然后将子模型并行分解，以近乎实时地预测收视率。通过从TripAdvisor收集的一个数据集，实验表明ClustPTF大大提高了建议的多样性（13。₀））和响应时间（TF _0的23.13x ）。甚至其预测性能也优于TF ₀（MAE改善了41.06％）。此外，在推荐多样性和预测性能（即MAE和精度）方面，ClustPTF的表现优于最新技术。