Context-aware Recommender Systems (CARS) deal with modeling and prediction of user interests and preferences according to contextual information while generating a recommendation. In contextual modeling- based CARS, the context information is used straight into the recommendation function as a predictor explicitly. Thus, this approach formulates a multidimensional recommendation model and is best realized through Tensor Factorization (TF) based techniques. It efficiently handles the data sparsity problem faced bymost of the traditional RS. However, the recent TF-based CARS face issues such as differentiating amongst relevant and irrelevant context variables, biased recommendations, and long-tail problem. In this paper, we propose a fusion-based approach for determining the list of most relevant and optimum contexts for two datasets, namely the LDos Comoda and Travel dataset. The mutual trust model that combines user level and item level trust is proposed further which utilizes the concept of trust propagation to calculate the inferred trust betweenusers/items. Finally, a hybrid reranking technique combining the item popularity and item absolute likeability reranking approaches with the standard ranking technique of generating recommendations is proposed to generate diversified recommendations. Comparative experiments on the LDos Comoda and the Travel datasets are conducted and the experimental results show an improvement of the proposed work with respect to RMSE of 50%, 55%, and 59% compared to MF-based RS, trust-based RS, and context-aware RS respectively. Also, the proposed reranking technique shows approximately three times more diversified recommendations than the standard ranking approach without a significant loss in precision.

Achieving a geometrically faithful, feature preserved, highly regular remesh with lower mesh complexity and high element quality is an ill-posed problem of surface remeshing in research. Individual surface remeshing techniques differ based on their end goals while ignoring the other enhancements in the remesh. In this research work, we present a surface remeshing framework that aim to balance the various crucial remeshing goals to enhance the remesh quality. In surface remeshing, the mesh quality comprises—(i) mesh complexity, (ii) mesh element quality, (iii) vertex regularity, (iv) geometric fidelity, and (v) feature preservation. Our remeshing approach uses the local edge operators to achieve mesh decimation, enhance element quality, and regularize the vertex valence of the remesh while conserving the features in the remesh. During mesh decimation, we preserve features using an updated quadric error metric. The mesh element quality is enhanced bysplitting maximal angles and uplifting the minimal angles using edge split and edge collapse respectively. We maintain dynamic priority queues to maintain the maximal and minimal angles that require attention and improve them using local edge operators. High vertex regularity is achieved by valence optimization. Thegeometric faithfulness of the remesh with the original input mesh is maintained by constraining the bounds on the approximation error computed by the two-sided Hausdorff distance. In succession with other local edge operators, our algorithm can remesh low-quality mesh surfaces efficiently. The remeshes generated using our remeshing framework were compared with recent remeshing approaches using various performance metrics.