Bat algorithm (BA), is a relatively new nature inspired metaheuristic algorithm, which works on the echolocation capabilities of micro-bats. Although being highly efficient, it suffers from pre-mature convergence. To ...
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Bat algorithm (BA), is a relatively new nature inspired metaheuristic algorithm, which works on the echolocation capabilities of micro-bats. Although being highly efficient, it suffers from pre-mature convergence. To overcome this limitation, this paper proposes a multimodal variant of BA, called multi-modal Bat algorithm (MMBA), which includes the foraging behaviour of bats. The standard BA exhibits a random movement for catching its prey. This work also proposes an enhancement to these exploration capabilities of bat, called Bat algorithm with Improved Search (BAIS). Each of these variants is tested for its efficacy against BA over 30 benchmark functions. An integration of both these modifications, the multi-modal Bat algorithm with Improved Search (MMBAIS), is also subsequently compared against the same 30 benchmark functions. Results established the superiority of MMBAIS over BA. Experimental comparison of MMBAIS with a recent variant of BA also revealed the efficiency of MMBAIS. (C) 2016 Elsevier B.V. All rights reserved.
A recommendation system is often used to recommend items that may be of interest to users. One of the main challenges is that the scarcity of actual interaction data between users and items restricts the performance o...
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A recommendation system is often used to recommend items that may be of interest to users. One of the main challenges is that the scarcity of actual interaction data between users and items restricts the performance of recommendation systems. To solve this problem, multi-modal technologies have been used for expanding available information. However, the existing multi-modal recommendation algorithms all extract the feature of single modality and simply splice the features of different modalities to predict the recommendation results. This fusion method can not completely mine the relevance of multi-modal features and lose the relationship between different modalities, which affects the prediction results. In this paper, we propose a Cross-modal-Based Fusion Recommendation algorithm (CMBF) that can capture both the single-modal features and the cross-modal features. Our algorithm uses a novel cross-modal fusion method to fuse the multi-modal features completely and learn the cross information between different modalities. We evaluate our algorithm on two datasets, MovieLens and Amazon. Experiments show that our method has achieved the best performance compared to other recommendation algorithms. We also design ablation study to prove that our cross-modal fusion method improves the prediction results.
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