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The ongoing expansion of commercial websites greatly increases the need of effective recommender systems for finding the solution to information overload problem. Conventional systems made use of explicit information obtained from users in the form of ratings and feedback queries which are highly affected by users' demographic location, age and attitude. The methodology used nowadays is to make use...
Recommender system is a tool that provides suggestions to customers. Recommendations are provided for the products that a customer may like in future or that are close to the target customer. On an e-commerce website good recommendation plays an important role for the seller and the buyer. So far researchers have digged out many methodologies for recommendation that may use explicit ratings or implicit...
During the last two decades we have witnessed the tremendous amount of growth in e-commerce industry. People all over the world buy articles just by a click of mouse. Today recommendation system is an important part of almost every website. A user might not be able to find out all the desired articles and items from the endless information pool available on the internet. Recommender system suggests...
We introduce a smart business-to-consumer (B2C) e-commerce system based on the artificial systems, computational experiments, and parallel execution (ACP) approach to intelligently understand customers' interests and provide a customized B2C service experience. Taking recommendation as an example, the behavioral experiments' result shows significant improvement of the proposed system.
Online shopping has grown rapidly over the past few years. Besides the convenience of shopping directly from ones home, an important advantage of e-commerce is the great variety of items that online stores offer. However, with such a large number of items, it becomes harder for vendors to determine which items are more relevant for a given user. Recommender Systems are programs that attempt to assist...
Recommender systems are gaining a great importance with the emergence of E-commerce and business on the internet. These recommender systems help users in making decision by suggesting products and services that satisfy the users' tastes and preferences. Collaborative filtering and content-based recommendation are two fundamental methods used to develop recommender systems. Although, both methods have...
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...
Recommender System is one of the most important technologies in E-commerce, and the collaborative filtering algorithm is the most widely used technique. In this paper, we proposed an improved collaborative filtering algorithm based on bipartite network, degree of nodes and sort of nodes both have been taken into account. And we only need to calculate the top-N similar neighbors for each target item,...
With the rapid development of e-commerce, online transactions has become an important part in people's lives, in order to support the smooth development of e-commerce activities, how to provide users with efficient and practical product information has become an urgent and critical problem. This paper presents a set of novel techniques based on page similarity measure, page clustering and wrapper...
Recent technological advances in many networks and applications, particularly the Internet and the World Wide Web (WWW), have generated a huge amount of information available to users. A recommender system in E-Commerce is an intermediary program (or an agent) with a user interface that automatically and intelligently generates a list of information which suits an individual's needs. In this paper,...
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,...
With the increasingly expanding of e-commerce scale, some problems, such as data sparsity and scalability problems, caused by the traditional collaborative filtering technology which is widely used in the recommender systems of e-commerce are becoming more and more prominent. At the same time, these problems decrease the recommender accuracy and influence the application effect of the recommender...
Recommender systems are very useful tools in application domains that suffer from information overload, offering the users suggestions they may be interested in. Owing to its business interest, e-commerce has become a major domain in this research field, since identifying those products that the users will appreciate could increase users' consumption. However, current e-commerce recommender systems...
Collaborative filtering is the most successful and widely used recommendation algorithm in E-commerce recommender systems currently. However, it faces severe challenge of cold-start problem. To solve the new item problem in cold-start, a cold-start recommendation method based on dynamic browsing tree model is proposed. Firstly, user browsing records are transformed to dynamic browsing tree (DBT) based...
Item-based and user-based collaborative filtering are two well-known algorithms for recommender system in e-commerce. Both the algorithms make use of similarity matrix whose elements represent the similarity of each item pairs or user pairs. A fast algorithm for item-based similarity matrix computation using cosine similarity metric was reviewed and applied for user-based one with some modification...
Recommenders systems are used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based ones recommend items similar to those a given user has liked in the past. Indeed, the past behavior is supposed to be a reliable indicator of her future behavior. This assumption, however, causes the overspecialization problem. Our purpose is to mitigate the problem...
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...
With the development of wireless networks and mobile terminals, mobile commerce has become a research hotspot in recent years for its commercial value and has been considered to be significant supplement and potential substitute of traditional e-commerce. Tourism, which has the feature of mobility, is a typical mobile commerce application. In this paper, we propose a RFID and personalized recommendation...
Collaborative filtering (CF) is one of the most successful technologies in recommender systems, and widely used in many personalized recommender applications, such as digital library, e-commerce, news sites, and so on. However, most collaborative filtering algorithms suffer from data sparsity problem which leads to inaccuracy of recommendation. This paper is with an eye to missing data imputation...
E-commerce companies have developed tools to assist users in finding the most suitable items for their needs or preferences. The most successful tool in this area has been the recommender systems. This kind of software obtains information about the users' tastes, opinions, necessities, and with a recommendation algorithm, infers recommendations that lead users to the most suitable items for them....
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