Based on our experience and expertise, we will offer you an impact analysis of coronavirus outbreak across industries to help you prepare for the future. Penetration of Advanced Gadgets to Boost Market The rising deployment of computing solutions in devices for applications such as emergency alerts, location tracking, and navigation will subsequently propel the growth of the market.
The rising penetration of smart gadgets such as tablets, PC, smartphones, and wearable devices will spur lucrative opportunities for the market in the foreseeable future. The growing investment in context-aware applications by companies will propel the growth of the market. The growing implementation of innovative technologies for customized solutions by companies such as Apple Inc. The unveiling of groundbreaking technology from Apple included Core ML, an advanced API to maneuver machine learning more efficiently on devices including iPhones, iPad, and others.
Furthermore, the growing integration of artificial intelligence AI to perform superior tasks will further enable the speedy expansion of the market. The growing investment in the development of AI by major players will augment the growth of the market. For instance, Google announced the acquisition of Deepmind, a major technology Provider company based in the UK.
The acquisition will aid the development of AI for e-commerce, games and other computer applications. IBM provides context-aware computing platforms to various industries including banking, financial, and insurance BFSI , retail, government, and others. The surge in users of context-aware technology will further improve the market in the region. The rising integration of Internet of Things technology in smart wearable devices will aid expansion in the US.
The growing adoption of smart homes along with the popularity of smart wearables will boost the market in North America. Moreover, the rising enterprise investments in context-aware applications and technologies by prominent players will promote growth in North America. The demand for context-aware computing applications for search engine recommendations and preference enhancement will foster the growth of the market.
For instance, Amazon, an E-commerce platform uses an intelligent recommendation engine to understand purchases based on individual preferences, shopping history, specific feature product interest, and others. TOC Continued!!! We tailor innovative solutions for our clients, assisting them address challenges distinct to their businesses. Our goal is to empower our clients with holistic market intelligence, giving a granular overview of the market they are operating in.
Our reports contain a unique mix of tangible insights and qualitative analysis to help companies achieve sustainable growth. Our team of experienced analysts and consultants use industry-leading research tools and techniques to compile comprehensive market studies, interspersed with relevant data.
We, therefore, offer recommendations, making it easier for them to navigate through technological and market-related changes. Our consulting services are designed to help organizations identify hidden opportunities and understand prevailing competitive challenges.
WHO recommends continued use of AstraZeneca vaccine. Latest coronavirus updates. Thousands of people from Myanmar sang songs and waved glow sticks as they gathered in Japan's capital on Thursday to protest the military coup in their home country. Mastercard has become the latest payment company to give cryptocurrencies its blessing. Teachers should be vaccinated, but schools can open safely if they aren't, says Miguel Cardona, the likely next education secretary.
Question: How do you fit candles onto a birthday cake for the world's second-oldest person? Ex-commander-in-chief refusing to apologise or express remorse for endangering veep's life in Capitol riot, says aide, according to report. Senator Josh Hawley was reported to have been seen with his feet up during proceedings. Adviser to former president says he 'loved' watching mob on 6 January.
Top news and what to watch in the markets on Thursday, February 11, Amazon is preparing to launch its own "digital currency" project in emerging markets, according to series of job adverts posted online. Amazon did not respond to requests for comment.
The tech giant has an Amazon Coins project that offers users discounts on purchases on Kindles and Fire tablets. It is not clear if the two projects are linked. This week, Twitter said it was considering adding Bitcoin to its balance sheet. Social media giant Facebook has made the biggest moves into digital coins, founding the Diem Association, previously known as Libra, to develop a cryptocurrency that would be used in a wallet developed by a Facebook company.
This could then be used to send money quickly and cheaply overseas or to buy products online. Since , Rep. Rosa DeLauro, D-Conn. Measures against nations such as Iran and Venezuela are hindering the global battle against coronavirus, say lawmakers Congresswoman Ilhan Omar is among the signatories of a letter to Joe Biden calling for an overhaul of the US sanctiosn regime.
In a letter to Joe Biden to be delivered on Thursday, 23 Democratic representatives and two senators, welcome a review of sanctions and their impact on the pandemic that the president launched soon after taking office. The letter signals the issue is likely to be at the heart of the foreign policy debate within the party in the Biden era.
It has already suspended a terrorist designation issued in the last days of Trump administration on the Houthis in Yemen, because of its impact on humanitarian aid deliveries. Often, risk-averse financial institutions are deterred from facilitating any transactions, even in humanitarian goods formally allowed by the sanctions. International Journal of Machine Learning and Cybernetics 10 :8, Applied Intelligence 49 :8, Knowledge and Information Systems 60 :2, Online publication date: 4-Jul Communications of the ACM 62 :8, Wang , Z.
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Harvesting Drug Effectiveness from Social Media. Health cards Consumer health search User study. Hierarchical Matching Network for Crime Classification. Retweet prediction Hot topics Social Medias. Reading comprehension Reading behavior model User behavior analysis Eye tracking.
Sentence classification Property identification Property embedding. Interpretable Fashion Matching with Rich Attributes. Fashion matching Information Retrieval Multimedia Recommendation. Learning to rank Sensitivity review Evaluation. Multi-view Embedding-based Synonyms for Email Search. Online Multi-modal Hashing with Dynamic Query-adaption.
Online multi-modal hashing Efficient discrete optimization Dynamic query-adaption Self-weighted. Outline generation Coherence Hierarchical structured prediction. Lifelogging Life event detection Personal knowledge base construction Social media.
Recommender system Fashion recommendation Collaborative filtering. Privacy-aware Document Ranking with Neural Signals. Neural ranking models Privacy and relevance tradeoffs Top K document search Learning-to-rank tree ensembles. Compatibility modeling Fashion analysis Neural networks Interpretable representation.
Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo and Yongfeng Zhang. Collaborative Filtering Attention Relation Learning. Online quality metrics Information retrieval evaluation Personal search Online experiments. Supervised Hierarchical Cross-Modal Hashing. Cross-modal Retrieval Hashing Hierarchy Discriminant analysis. Tasks Task completion Intelligent assistants. Reading behavior Retrieval model Reinforcement learning.
Recommendation Transparency Scrutability. Unified Collaborative Filtering over Graph Embeddings. Unsupervised Neural Generative Semantic Hashing. Unsupervised semantic hashing Deep learning Generative model Document ranking.
User Attention-guided Multimodal Dialog Systems. Why do Users Issue Good Queries? Neural Correlates of Term Specificity. Knowledge Discovery and Data Mining …. Yong Zheng - Google Scholar Citations. This "Cited by" count includes citations to the following articles in Scholar. Meena2, Preetesh Purohit3 1 M.
A context-aware personalized travel recommendation system Context-aware recommendation systems attempt to address the challenge of identifying products or items that have the greatest chance of meeting user requirements by adapting to current contextual information.
Negar Hariri - Academia. Context-Aware Recommendation Based On Review Mining more by Robin Burke , Bamshad Mobasher , and Negar Hariri Recommender systems are important building blocks in many of today's e-commerce applications including targeted advertising, personalized mar- keting and information retrieval. Context-aware recommender systems. Thanks, The Blackle Team. Web
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The summary of the related work of our proposed model and algorithm and the conclusion of the paper are given in Section 6. After the introduction of the collaborative filtering algorithm, scholars have more research on the recommendation system. The most important recommendation algorithms are a collaborative filtering algorithm and content-based recommendation algorithm [ 18 ].
However, there are certain problems hard to be solved in the two basic algorithms, such as collaborative filtering algorithm has a cold start and high sparsely, and content-based recommendation algorithm is difficult to deal with multiple attributes. Therefore, the accuracy of a single recommendation algorithm recommendation is difficult to improve [ 19 ].
Many scholars have shifted their goals from traditional recommendation algorithms, focusing on the research of context-based recommendation systems [ 20 ]. Foreign researches have been quite rich. The research direction and application fields include shopping, tourism, catering, and other aspects.
But they focused on proposing improved algorithms, which are based on traditional algorithms. The contrast of advantages and disadvantages of various algorithms based on context perception is relatively rare. The fields and data types applicable to different context-aware algorithms are not studied.
Among them, Kang et al. And they used packet sniffing technique. Tuan et al. Lee et al. Cai et al. Ji et al. Wang et al. Unger et al. The so-called context information is a general concept, which can be divided into user context information, physical context information, time context information, network context information, and so on. The physical context information includes geographical location, temperature, and weather. The time context information includes season, morning or evening, and weekend or working day.
The acquisition of this context information is conducive to better recommendation. If the system had those contexts, it could recommend suitable advertisements according to the geographical location, suitable music according to emotions, and decide whether videos are automatically loaded according to the network speed.
As shown in Figure 1 , the types of context information currently defined are very diverse, but not all context information is readily available. The application of the context-aware algorithm is based on context acquisition. The shallow-level situation acquisition technology can already be achieved. Deep context information is helpful for the recommendation algorithm.
Related research on the context information recommendation is increasing, and the situation plays an important role in the personalized recommendation. Under the scope of user interests, there are some drifts in different context interests, and better recommendation can be obtained by studying the changes in the situation. At present, the context-aware model has a relatively consistent definition. Context information is a collection of information about users, generally denoted by , which is , representing a set of contexts.
And denotes a context element in the set, while denotes a common context element. The classification of context information has been as described in Section 3. Some of the context in Section 3. The influence of some context information on the recommendation model can be explored.
Through filtering and calculating, they can be applied to personalized recommendations to provide with more accurate recommendations. As showed in Figure 2 , the context-awareness recommendation model can be divided into three layers.
The first layer is the data acquisition layer, which collects various types of data of users. The second layer is the data processing layer, which filters and processes the data. The third layer is the recommended service layer, which is operated by the algorithm to form a personalized recommendation.
The association of services at each level provides a means of contextual awareness. In the traditional recommendation algorithm, the score matrix is associated with user and item, called user-item model, expressed as. The above formulas constitute a two-dimensional score matrix between the user and the item and can be operated using a collaborative filtering algorithm to obtain a recommended result.
In the context-awareness recommendation model, the context factor is introduced, which is no longer a two-dimensional model, that is, it becomes a user-item-context model, expressed as. In the three-dimensional model, the score is represented as each of the small cubes that are segmented out. The cube in the three-dimensional coordinate system is the set of scoring data R.
Based on this theory, the algorithm experiment can be further developed. When exploring the context factors, this paper selects three types of situations for classification. There is an inclusion relationship between single-situation factors and multisituation factors, such as context at home , including weekend, weekday, and holiday , and alone, with friends, with family, and with lovers.
It is actually compound context information. Through the aggregation of context information, it is explored whether there is consistency in user selection in the context of aggregation. This algorithm is a context-based prefiltering algorithm, which is a process of context selection. The algorithm aims at transforming the three-dimension model into a two-dimension model through context filtering.
The score prediction function, should be represented as the following formula 1 : where represents the score matrix in the three-dimensional model, and the threshold of the context set indicates that the context information is. After getting the score matrix, the similarity between users is calculated. The Pearson correlation coefficient calculation should be represented as the following formula 2 : where represents the score of a user for item. By calculating their similarities, the similarity matrix is formed as below:.
The predicted score determines the degree of user interest. The higher the score is, the more the user is interested in. The process of Algorithm 1 can be summarized as follows:. In the algorithm proposed above, the context information is a separate factor. The context information is not incorporated into the algorithm model, so the contextual modeling algorithm is proposed.
The contextual modeling algorithm is divided into heuristic and model. There are many literature and papers in the research of contextual modeling algorithms based on models. It is not the focus of this paper. The algorithm proposed in this paper integrates the context information and processes three-dimension model. We propose a user-based contextual recommendation model. The user-based context is scoring matrix is constructed, and the method and steps are the same as before.
In the algorithm, we use user as an example to predict its score on the item in the context. The nearest neighbor of user needs to be found, and the user similarity matrix can be obtained by substituting the required data into the formula. Among all the obtained user similarities, the top-N similar users are selected as the nearest neighbors, and the score of the item under the situation is predicted based on the score data of these users.
In order to obtain the rating of the user under the context , we find the rating context set of the most neighboring user for the item. The weighting operation is performed in combination with the user similarity and the context similarity obtained in the foregoing. The result of the scoring can be obtained. The algorithm is described as following:. The user-based heuristic contextual model is discussed above, and the algorithm to be discussed is similar with that.
Item-based contextual recommendation model is also a recommendation algorithm based on context modeling. We calculate the average score of the same item in the same situation. Then, we calculate context similarity based on items using Pearson formula. The nearest neighbor of user needs to be found, and the item similarity matrix can be obtained by substituting the required data into the formula.
Among all the obtained item similarities, the top-N similar items are selected as the nearest neighbors, and the score of the item under the situation is predicted based on the score data of these items. In order to obtain the rating of the item under the context , we find the rating context set of the most neighboring item for the context.
The weighting operation is performed in combination with the item similarity and the context similarity obtained in the foregoing. The hardware environment of this experiment is the Intel Core i5 processor, 8G memory. The data of this experiment is mainly from the questionnaire survey of college students about the context information of watching movies.
In order to guarantee the accuracy of the algorithm, some invalid questionnaires are eliminated. The data of 96 users were retained, including data. The quality of the recommendation system is mainly measured by some indicators. The accuracy of the prediction is extremely important in the recommendation system. The root mean square error and the mean absolute error are both the degree of deviation between the calculated predicted value and the true value.
The value obtained by subtracting the predicted value from the true value is recalculated, and the larger the obtained result value, the greater the degree of deviation. On the contrary, the more accurate the prediction of the algorithm is. When judging the merits and demerits of the algorithm according to these two parameters, the value is expected to be small.
The traditional prediction algorithm is set as the control object of this experiment, so that the latter experiment is compared with the experimental results obtained by the two-dimensional algorithm. In this paper, the user-based collaborative filtering algorithm is selected. According to the Pearson similarity coefficient, the nearest neighbor of the movie is calculated and we use them to predict.
Several classical collaborative filtering algorithms and its improved algorithms are selected, which are KNN basic, KNN means, KNN Zscore, and KNN baseline, respectively, which represent the basic collaborative filtering algorithm, the collaborative filtering algorithm based on the scoring average, the collaborative filtering algorithm based on scoring standardization, and the collaborative filtering algorithm based on baseline scoring.
At the same time, in order to avoid the contingency error, each of the following experiments set a five-fold cross-validation, and the obtained results were evaluated using the values of RMSE and MAE. In the collaborative filtering algorithm, the recommended precision depends on the number of nearest neighbors. In this paper, four values are selected as the representative, and the different values represent the maximum number of nearest neighbors.
According to Tables 1 and 2 , the KNN baseline algorithm the collaborative filtering algorithm based on the baseline score is obviously inferior to the other algorithms, indicating that it does not fit for the problem. So, we omit it in the following experiments. It can be observed in Figures 3 and 4 that the pros and cons of the algorithm are not always the same.
Under different values, the prediction effect of the recommendation algorithm changes. Overall, the increase of the maximum number of neighbors is accompanied by the prediction accuracy. It rises first and then falls. If the value is selected approximately, the prediction works best. The basic collaborative filtering has larger RMSE and MAE values when the value is smaller, but it shows an advantage when the k exceeds The other two algorithms also show advantages when is less than 15, and the collaborative filtering algorithm based on scoring standardization is always better than the collaborative filtering algorithm based on scoring average.
In order to implement the recommendation algorithm based on the specific context, we initially screened and organized the data set. As shown in Figure 5 , the context information has an inclusive relationship. The collection of context information is like a forest. Here, we choose three attributes of watching movies, and they are time, place, and company, forming three trees. There are parent nodes and their branches, each of which, respectively, represents the specific movie-watching behavior.
According to the Table 3 , this resulted in an aggregation of six different contexts, namely, at home, at the cinema, one person, on weekends, on weekdays, and on holidays. The selected situation has to be in conformity with the logic of reality. Usually, in this context information, the frequency of people watching movies will increase.
For each context environment, we use the collaborative filtering algorithm to predict and get the recommended results in this context. For the different nearest neighbors number, we will get different results in Tables 4 and 5. The performance of several selected contextual effects is different, in order to more intuitively observe the advantages and disadvantages of the algorithm and compare with those traditional collaborative filtering algorithm.
Through the above experimental results, it can be observed that the smaller the value of RMSE and MAE, the better the prediction effect. Conversely, the recommended algorithm is less effective. In Figure 6 , the solid line indicates the result obtained by the conventional algorithm, and the broken line indicates the result obtained by the improved precontextual filtering algorithm.
In different situations, the performance of the algorithm is not the same. In the above six contexts, there are four contexts that are better than the traditional algorithm regardless of the value of the nearest neighbor.
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