The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Social market, consumers frequently connect from ecommerce websites to social networking sites such as Facebook and Twitter. There have been few determinations on accepting the connections between users' community media profiles and their e-commerce activities. Consumers can also post their newly bought products on micro blogs with links to the e-commerce product web pages. Review on Prediction user's...
India is gradually moving towards paperless work for which all the projects whether governmental or e-commerce are moving online. It leads to increase huge amount of data and information on the internet. Searching for a particular or preferred service or product through this vast amount of data is the hell of a job for a client or a customer. It spoils a lot of precious time and in this busy world...
Software applications such as e-services need to obtain knowledge about their users. This is essential in many platforms in which the user has to get the comfort and easiness of a more personal feeling. Examples of these platforms are intelligent e-commerce applications, intelligent tutoring systems, intelligent agents, adaptive multimedia systems and search engines. The purpose of this paper is to...
In recommendation systems, the relationship between information size and recommendation performance is an important research point. Here, we study this relationship based on a new method, variable precision, and design a new algorithm. We demonstrate that recommendation systems perform better with higher data precision, however which should be controlled within a threshold. We collect movie rating...
The phenomenon of information overloading is increasingly severe with the development of e-commerce websites. It is an urgent issue that how to make users find information they need efficiently in the huge information space and at the same time make e-commerce enterprises enhance their websites' attraction and sales effectively. The personalized e-commerce recommendation system is an effective method...
Personalized recommendation in e-commerce means that when a user visits one website, the website will provide the user as personalized service as recommending online some pages that might be interesting for the user according to the user's clustering features. This paper shows the structure of personalized recommendation system in e-commerce. It analyzes the collaborative filtering technology used...
Nowadays, recommender systems are considered as one of the basic pillars of e-commerce as they help users to take decisions easily. These systems involve a multitude of techniques ranging from hybrid filtering mechanisms to techniques derived from statistics or artificial intelligence. In the present paper, we put forward an improved recommender system that supports ethics in an automatic way without...
Collaborative filtering is one of the most important technologies in e-commerce recommendation system. Traditional similarity measure methods work poorly when the user rating data are extremely sparse. Aiming at this issue a hybrid collaborative filtering is proposed. This method used a novel similarity measure method to predict the target item rating and it fused the advantages of the user-based...
Delay Tolerant Networks (DTNs) have been identified as one of the key areas in the field of wireless communications. They are characterized by large end-to-end communication latency and the lack of end-to-end path from a source to its destination. These characteristics pose several challenges to the security of DTNs. Especially, Byzantine attacks give serious damages to the network in terms of latency...
Through the wide use of E-commerce, the acquisition of personalized need is key to effective recommender. From the view of knowledge acquiring, case intelligence is a comprehensive expression which is integrated representation of human sense, logics and creativity, and can acquire the user's preferences from the former stored cases. As the E-commerce is under much complex conditions, this paper presents...
The core of the classic collaborative filtering algorithms about similar calculation are designed on the basis of the “user-item matrix” model. This paper proposes an improved collaborative filtering algorithm on the basis of the “user-item cube” model, which takes care of the factor of the data produced when the user purchased the item. The algorithm attaches the corresponding weight to the date...
Typical trust factor based collaborative filtering algorithm is not suitable for user's multiple interest recommendation. A new algorithm combines traditional trust factor-based collaborative filtering with similar item-based collaborative filtering was presented. The experimental results show that the proposed methods perform better than the old recommendation method.
This paper applied Multi-Agent to E-commerce personalized Recommender System, and designed E-commerce personalized Recommender System based on Multi-Agent, namely, MAPRS. Off-line recommendation and on-line hybrid recommendation are used to construct the core recommender model under the intelligent control. The paper presents the function and design ideas of various components of the system.
More and more E-commerce Websites provide the products with different price which made it hard for consumers to find the products and services they wanted. In order to overcome the information overload, personalized recommendation systems were proposed to suggest products and to provide consumers with information to help them decide which products to purchase. Personalized recommendation systems can...
At present, most e-commerce recommendation systems act only as just a single tool and provide a single recommendation model. However, due to the complexity of e-commerce system itself, different occasions and customers of different identities require different types of recommendation services. In the paper, we study the architecture of complex e-commerce which can collect multiple types of data, use...
Collaborative filtering technology is the key technology of recommendation system. However, collaborative filtering technology has been suffering from sparsity that it needs mass ratings from users to improve precision. In traditional e-commerce, asking users to rate on their own initiative will degrade experience of users, let alone the mobile business environment. So, both in e-commerce and m-commerce,...
Collaborative filtering is a very important technology in e-commerce. Unfortunately, with the increase of users and commodities, the user rating data is extremely sparse, which leads to the low efficient collaborative filtering recommendation system. To address these issues, an optimized collaborative filtering recommendation algorithm based on item is proposed. While calculating the similarity of...
Recommendation systems, best known for their use in e-commerce or social network applications, predict users' preferences and output item suggestions. Modern recommenders are often faced with many challenges, such as covering high volume of volatile information, dealing with data sparsity, and producing high-quality results. Therefore, while there are already several strategies of this category, some...
Collaborative Filtering is a very important technology in E-commerce. Unfortunately, with the increase of users and commodities, the user rating data is extremely sparse, which leads to the low efficient Collaborative Filtering recommendation system. To address these issues, many methods of processing no-rated items in Collaborative Filtering recommendation algorithm have been proposed, including...
Although utility-based recommendation in e-commerce can provide much better recommendation accuracy, there are still no effective approaches to build the utility function of each user. In order to overcome this problem, an approach based on Bayesian networks is proposed. Firstly, based on the common user utility function of a specific commodity which has already been constructed by domain experts,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.