scispace - formally typeset
Search or ask a question

Answers from top 7 papers

More filters
Papers (7)Insight
It would seem that Movie Tourism could be a new form of cultural landscape.
William Reeves is thus a quintessential Californian, a fact of which, all who know him realize, he is quietly very proud.
Thus every eventually nonnegative matrix is similar to a seminonnegative matrix.
We further demonstrate that, if A is a Euclidean distance matrix, then B is a spherical Euclidean distance matrix.
By carefully reusing certain intermediate quantities from one step to the next, we can efficiently compute such a sequence of matrix exponentials.
Open accessProceedings ArticleDOI
01 Apr 2012
390 Citations
Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the Movie Lens movie recommendation system reveals that LARS is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.
We show that, for suitable choices of the matrix coefficients P and Q, it is possible to characterize by means of φ ( z ) well known matrix functions, namely the matrix square root, the matrix polar factor, the matrix sign and the geometric mean of two matrices.

See what other people are reading

What factors should be considered when recommending a strand to students?
4 answers
When recommending a strand to students, several factors should be considered. Firstly, the student's profile, including sex, household income, parental education, and occupational field, plays a significant role in their choice of strand. Additionally, understanding the relationship between the program and student context is crucial for making appropriate recommendations, as highlighted in a study involving 885 students from various universities. Moreover, students' preferences based on passion or practicality should be taken into account, as these factors influence their decision-making process. Lastly, the impact of specialized programs on students, such as dual immersion programs, should be considered to ensure that students are adequately supported academically and socially when transitioning to a new program.
Personalized Nutrition Coaching?
5 answers
Personalized nutrition coaching involves tailoring dietary advice based on individual factors like genetics, lifestyle, and preferences. This approach aims to promote health, prevent disease, and induce lasting dietary behavior changes. Studies show that personalized nutrition programs, combined with coaching by certified experts, can effectively improve glycemic control, aid in weight management, and reduce cardiovascular risk factors in individuals with Type 2 Diabetes. The evolution of personalized nutrition services has been driven by advancements in technology, scientific data analysis, and consumer interest in health-related offerings. As the field continues to grow, personalized nutrition is expected to play a significant role in public health by providing tailored dietary recommendations and promoting overall well-being.
Web Based Nutrition Coaching ?
5 answers
Web-based nutrition coaching, facilitated through platforms like mobile apps and web applications, offers personalized dietary advice and support to users. These systems leverage artificial intelligence to provide tailored recommendations based on individual factors such as allergies, eating habits, and lifestyle. They aim to improve diet quality, promote healthy eating behaviors, and enhance overall well-being. Studies have shown that web-based nutrition counseling can positively impact dietary intake, weight control, and quality of life for cancer patients and survivors. Additionally, these platforms have been effective in improving unhealthy behaviors, such as sedentariness, through remote coaching approaches and interactive tools like serious games and food monitoring apps. Overall, web-based nutrition coaching represents a promising approach to enhancing nutritional outcomes and promoting healthier lifestyles in diverse populations.
What are the most effective methods for filtering topics in research studies?
5 answers
The most effective methods for filtering topics in research studies involve utilizing advanced models like Pattern-based Topic Model (PBTM) and Maximum matched Pattern-based Topic Model (MPBTM) proposed by Yang Gao, Yue Xu, Yuefeng Li. These models combine topic distributions with semantic pattern representations to accurately represent documents and rank document relevance. Additionally, a solution proposed by Yang Xing involves automatically detecting topics using Latent Dirichlet Allocation and filtering out irrelevant information to understand the evolution of research topics. Furthermore, a rough set aided method proposed by Hong Guo, Yunda Cao, and Song Guo incorporates variant weighting methods and twice-filtering techniques to speed up topic-specific text filtering with high precision and recall. These methods collectively offer comprehensive and efficient approaches to filter topics in research studies.
What are some of the key trends and innovations in the fashion industry that are driving business intelligence?
5 answers
Key trends and innovations in the fashion industry that are driving business intelligence include the integration of modern technologies like big data, artificial intelligence (AI), cloud computing, and blockchain. Fashion companies are increasingly utilizing AI applications for data-driven decision-making, leading to significant impacts on consumer behavior and operational strategies. Moreover, the industry is experiencing a paradigm shift towards leveraging massive fashion data for competitive advantage, with a focus on areas such as 3D fashion design, RFID technology, virtual showrooms, and online shopping. Additionally, the rapid growth of the online fashion market has spurred the development of AI-driven services like visual search and recommender systems, enhancing the overall customer experience and engagement. These trends collectively highlight the transformative role of technology and data analytics in shaping the future of the fashion industry.
What are some effective personalization techniques used in e-commerce to enhance the shopping experience for customers?
5 answers
Effective personalization techniques in e-commerce include AI-driven customer behavior prediction and personalized recommendations. Another technique is the use of Web personalization to enhance user perceptions and influence purchase decisions. Additionally, e-commerce platforms utilize personalized recommendation systems based on machine learning to combat information overload and offer tailored product suggestions in real-time. Furthermore, the Online Constrained Multiple-Choice Promotions Personalization framework leverages uplift modeling to optimize promotions for individual customers while adhering to budget constraints, resulting in improved sales and long-term promotional campaign sustainability. These techniques collectively aim to enhance user engagement, improve shopping experiences, and drive conversions in the competitive e-commerce landscape.
What are convolution operations in image processing?
5 answers
Convolution operations in image processing involve applying a filter or kernel to an image to extract features or enhance certain characteristics. This process includes dividing the image into sub-images, performing convolution on these sub-images, and then combining the results to obtain the final convolution output. By convolving the image with a kernel, specific operations like smoothing, sharpening, noise reduction, and edge detection can be achieved. The convolution operation method typically entails multiplying elements of partial matrices from the image and the kernel, summing these products, and generating a convolution result. Utilizing convolutional neural networks in image processing allows for cutting the image into sub-images, independently convolving them, aggregating the results, and outputting the final processed image, enhancing system performance.
What are the current trends and developments in the new media industry?
4 answers
The current trends and developments in the new media industry include the decline of traditional terrestrial television due to the emergence of online entertainment platforms and services. Artificial intelligence (AI) is playing a significant role in the news industry, with applications in news gathering, content production, distribution, and consumer consumption. The shift from text-based news to visual news in the era of new media has had a profound impact on news communication, with organizations and individuals becoming active participants in news dissemination. The media industry is undergoing rapid transformation, driven by factors such as changes in consumption patterns towards digital media products, the rise of new media producers, the development of media corporations, and the increasing importance of internet sources and online commerce. The application of new media technologies has also impacted traditional TV news media, presenting both challenges and opportunities.
What are the different techniques used for personalization in marketing and e-commerce?
4 answers
Different techniques used for personalization in marketing and e-commerce include: real-time website personalization based on user behavior and preferences, utilizing topic-models and neural networks for banner recommendation; multi-feature based classification of web pages into categories, modeling and quantifying user interests and preferences, personalized search systems, and hybrid recommender systems for news recommendation; customization techniques such as rule-based, collaborative filtering, and information discovery in e-commerce, with the objective of using customer preferences for business purposes; recommendation systems employing content-based filtering, collaborative filtering, and hybrid techniques to provide personalized shopping experiences; and hyper-personalization strategies using machine learning and artificial intelligence techniques for marketing functions like segmentation, targeting, and positioning based on real-time analytics throughout the customer journey.
How does the concept of perceived usefulness influence consumer behavior in making purchasing decisions?
5 answers
Perceived usefulness plays a significant role in influencing consumer behavior in making purchasing decisions. It has been found that the perceived usefulness of personalized recommendations strongly affects purchase intention. Additionally, perceived usefulness directly affects online purchase intention in the context of Samsung's folding screen phones. Furthermore, the perceived usefulness of online shopping platforms has a positive impact on college students' willingness to make online purchases. In the case of BNI Tapcash e-toll cards, perceived usefulness also influences purchase decisions, with a positive and significant effect. Moreover, perceived usefulness is one of the predictors of online shopping behavior, along with perceived risk and perceived ease of use. Overall, the concept of perceived usefulness is consistently shown to have a positive influence on consumer behavior and purchase decisions in various contexts.
How effective are web-based skills assessment systems in predicting job outcomes for college students?
5 answers
Web-based skills assessment systems have shown potential in predicting job outcomes for college students. These systems provide a mechanism for rating prediction by considering feedback from graduates who have job offers and have provided ratings to employers. By calculating the similarity between students, these systems generate a list of potential employers for students, taking into account their interests as well. Experimental studies on real recruitment datasets have demonstrated the effectiveness of these systems in helping students find proper jobs and enhancing their employability skills. Additionally, these systems can assist teachers and tutors in monitoring student learning, identifying areas of improvement, and checking achievements. They can also be used in the recruitment process of companies, aiding in the selection of candidates for jobs. Overall, web-based skills assessment systems have the potential to significantly impact job outcomes for college students.