This paper addresses the problem of multi-criteria recommendation in the hotel industry. The main focus is to analyze user preferences from different aspects based on multi-criteria ratings and develop a new multi-criteria collaborative filtering method for hotel recommendations. Particularly, the proposed recommendation system integrates matrix factorization into a deep learning model to predict the multi-criteria ratings, and then the evidential reasoning approach is adopted to model the uncertainty of those ratings represented as mass functions in Dempster-Shafer theory of evidence. Finally, Dempster’s rule of combination is utilized to aggregate those multi-criteria ratings to obtain the overall rating for recommendation. Extensive experiments conducted on a real-world dataset demonstrate the effectiveness and efficiency of the proposed method compared with other multi-criteria collaborative filtering methods.

The evaluation of innovation capability (IC) plays a key role in an age of keen competition driven by modern technologies, since it enables organizations to review their innovation management process and to adjust their corresponding innovation policies. Moreover, the IC evaluation is, in fact, a multicriteria process with high uncertainty, since the market environments and competitors’ performance are both in a dynamic environment. Therefore, the evaluation of IC under uncertainty is vital to organizations. This study proposes a new integrated method for the evaluation of IC in banking organizations by combining the analytic hierarchy process (AHP) and the evidential reasoning (ER) approach in terms of the Dempster–Shafer theory of evidence. Three Vietnamese banks were used as a case study to demonstrate the applicability and validity of the proposed method. Experts in banking-related fields were invited to determine the relative importance weights of critical innovation management practices (CIMPs) and their sub-CIMPs using the AHP and to score the maturity levels of sub-CIMPs at the evaluated banks. The ER approach was then applied to generate aggregated assessments representing the ICs of banks that were finally used for their ranking in terms of IC.

This paper addresses the problem of fuzzy clustering for categorical data. During the last two decades, many attempts have been made to extend the $k$ -means algorithm, making it applicable to clustering for categorical data, due to its simplicity and efficiency. However, as $k$ -means-like algorithms are local optimization methods, their clustering results are highly sensitive to initialization. In this paper, we propose to use Locality-Sensitive Hashing (LSH) to reduce the categorical data dimensions and predict the initial fuzzy clusters in low-dimensional space. Different from the existing cluster initialization optimization methods that aim to create only crisp initial clusters, the proposed method aims at predicting ‘high quality’ fuzzy clusters at the initialization step before proceeding in the $k$ -means-like fashion. The numerical results show that the proposed method yields relatively accurate results on 16 UCI datasets and outperforms all other related approaches in terms of both crisp and fuzzy clustering effectiveness.

It is challenging to build a real-time information retrieval system, especially for systems with high-dimensional big data. To structure big data, many hashing algorithms that map similar data items to the same bucket to advance the search have been proposed. Locality-Sensitive Hashing (LSH) is a common approach for reducing the number of dimensions of a data set, by using a family of hash functions and a hash table. The LSH hash table is an additional component that supports the indexing of hash values (keys) for the corresponding data/items. We previously proposed the Dynamic Locality-Sensitive Hashing (DLSH) algorithm with a dynamically structured hash table, optimized for storage in the main memory and General-Purpose computation on Graphics Processing Units (GPGPU) memory. This supports the handling of constantly updated data sets, such as songs, images, or text databases. The DLSH algorithm works effectively with data sets that are updated with high frequency and is compatible with parallel processing. However, the use of a single GPGPU device for processing big data is inadequate, due to the small memory capacity of GPGPU devices. When using multiple GPGPU devices for searching, we need an effective search algorithm to balance the jobs. In this paper, we propose an extension of DLSH for big data sets using multiple GPGPUs, in order to increase the capacity and performance of the information retrieval system. Different search strategies on multiple DLSH clusters are also proposed to adapt our parallelized system. With significant results in terms of performance and accuracy, we show that DLSH can be applied to real-life dynamic database systems.