Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work

Is Google Duplex too human? : exploring user perceptions of opaque conversational agents

Conversational Agents (CAs) are increasingly embedded in consumer products, such as smartphones, home devices, and industry devices. Advancements in machine generated voice, such as the Google Duplex feature released in May 2018, aim to perfectly mimic the human voice while constructing a scenario in which users do not know whether they are talking to a human or a CA. Exactly how well users can distinguish between human/machine voices, how the degree of humanness impacts user emotional perception, and what ethical concerns this raises, remains an underexplored area. To answer these questions, I collected 405 surveys, including both an experimental design that exposed users to three different voices (human, advanced machine, and simple machine) and questions about the ethical implication of CAs. Results of the experiment revealed that users have difficulty distinguishing between human and advanced machine voices. Users do not experience the negative feeling referred to as the uncanny valley when listening to advanced synthetic audio and they only narrowly prefer a real human voice over a synthetic voice. Results from the questions about ethical implications revealed the importance of context and transparency. Drawing on these findings, I discuss the implications of advanced CAs and suggest strategies for ethical design.Journalis

Directive deficiencies: How resource constraints direct opportunity identification in SMEs

Previous studies show that resource constraints have mixed effects on innovation and opportunity identification by entrepreneurs. Sometimes, resource constraints lead to identifying more opportunities, whereas in other cases entrepreneurs rather see fewer opportunities. This study explores a new approach to reconcile this inconsistency. Using a sample of 219 small and medium-sized enterprises (SMEs), we explore relationships between supply and demand constraints and identifying supply and demand opportunities. The results show that supply constraints have a positive effect on identifying supply opportunities, but a negative effect on identifying demand opportunities. Similarly, demand constraints have a positive effect on identifying demand opportunities, but a negative effect on identifying supply opportunities. Thus, this study shows that resource constraints direct the entrepreneur’s attention towards opportunities inside the constrained domain rather than outside the constrained domain. An important consequence for theory is that a complete explanation of the mixed effects should consider different types of resource constraints and different sources of opportunities simultaneously. For practicing entrepreneurs, being aware of this mechanism can prevent that they miss out on promising opportunities outside the constrained domains

If deep learning is the answer, then what is the question?

Neuroscience research is undergoing a minor revolution. Recent advances in machine learning and artificial intelligence (AI) research have opened up new ways of thinking about neural computation. Many researchers are excited by the possibility that deep neural networks may offer theories of perception, cognition and action for biological brains. This perspective has the potential to radically reshape our approach to understanding neural systems, because the computations performed by deep networks are learned from experience, not endowed by the researcher. If so, how can neuroscientists use deep networks to model and understand biological brains? What is the outlook for neuroscientists who seek to characterise computations or neural codes, or who wish to understand perception, attention, memory, and executive functions? In this Perspective, our goal is to offer a roadmap for systems neuroscience research in the age of deep learning. We discuss the conceptual and methodological challenges of comparing behaviour, learning dynamics, and neural representation in artificial and biological systems. We highlight new research questions that have emerged for neuroscience as a direct consequence of recent advances in machine learning.Comment: 4 Figures, 17 Page