Critical Factors Predicting The Acceptance Of Digital Museums: User And System Perspectives

Digital museums are replacing traditional museums to inspire individual growth and promote culture exchange and society enrichment. However, the benefits of using the traditional museum to inspire visitors and promote the local economy may be compromised in the digital museum. This study attempts to offer insights on digital museum adoption from user and system perspectives. We extended the technology acceptance model (TAM) by incorporating computer self-efficacy and personal innovativeness as individual variables and media richness as a system characteristic. We launched a full-scale study with 441 users of 3 weather museums in Taiwan. We had 327 valid responses, a 74% response rate, from our target population. We conducted a regression analysis to investigate the potential influence of independent variables on the adoption of digital museums. Our results showed that both user and system characteristics have a positive influence on perceived usefulness (PU). A proper consideration of these three constructs can increase a user’s PU and perceived ease of use (PEOU), thereby establishing a more positive attitude regarding the use of digital museums. Academic and practical implications concerning their adoption from user and system perspectives were drawn from these findings

Sequeval: an offline evaluation framework for sequence-based recommender systems

Recommender systems have gained a lot of popularity due to their large adoption in various industries such as entertainment and tourism. Numerous research efforts have focused on formulating and advancing state-of-the-art of systems that recommend the right set of items to the right person. However, these recommender systems are hard to compare since the published evaluation results are computed on diverse datasets and obtained using different methodologies. In this paper, we researched and prototyped an offline evaluation framework called Sequeval that is designed to evaluate recommender systems capable of suggesting sequences of items. We provide a mathematical definition of such sequence-based recommenders, a methodology for performing their evaluation, and the implementation details of eight metrics. We report the lessons learned using this framework for assessing the performance of four baselines and two recommender systems based on Conditional Random Fields (CRF) and Recurrent Neural Networks (RNN), considering two different datasets. Sequeval is publicly available and it aims to become a focal point for researchers and practitioners when experimenting with sequence-based recommender systems, providing comparable and objective evaluation results

A multimedia recommender integrating object features and user behavior

Despite the great amount of work done in the last decade, retrieving information of interest from a large multimedia repository still remains an open issue. In this paper, we propose an intelligent browsing system based on a novel recommendation paradigm. Our approach combines usage patters with low-level features and semantic descriptors in order to predict users’ behavior and provide effective recommendations. The proposed paradigm is very general and can be applied to any type of multimedia data. In order to make the recommender system even more flexible, we introduce the concept of multichannel browser, i.e. a browser that allows concurrent browsing of multiple media channels. We implemented a prototype of the proposed system and tested the effectiveness of our approach in a virtual museum scenario. Experimental results have proved that the system greatly enhances users’ experience, thus encouraging further research in this direction

Multicriteria Evaluation for Top-k and Sequence-based Recommender Systems

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