Search results

User interests in electronic commerce reflect the behavior set of users acting on certain impulse. Electronic commerce web stations (ECWS) might make full use of intelligent IT to create and refine user interests database (UIDB) to make services personalized. In this paper, the issues about how to implement such kind of services are investigated, the concepts of user interests and their transitions are defined; the structure of UIDB and how to create it are explored, and the analysis and mining of the data in server log files to help dynamically updating UIDB are discussed in detail. Then, ECWS can actively recommend suitable series of pre‐sent web pages for different users and flexibly deal with transitions of users' interests. The dynamic structure may make the system perfect after a period of use to help ECWS to offer their users with personalized service.

The user interest in content searching in the web will be changed over by time.

The system is in need to find the content of user over the temporal aspects.

So, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important.

So, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important.

In this work, fuzzy neural network techniques are used to predict the user interest fluctuation in different times in different scenarios.

In this proposed work, both the long-term and short-term interest are evaluated using the specialized user interface designed to retrieve the user interest based on the user searching activities.

This work also categorizes the future needs of users using this proposed system.

In electronic commerce (EC), user interest reflects the behavior set of a users’ group acting on certain impulses. Electronic commerce web stations (ECWS) might be useful for intelligent information technology to create and refine the user interests database (UIDB) to make all kinds of service personalized. Usually two types of information should be included in UIDB. The first is contents of products or services and the second is forms for showing the contents. Both their structures are tangled trees. The issues about how to implement personalized service were investigated, the concepts of user interests and the structure of UIDB are defined, those about ECWS, how to create UIDB by user answers' selections, update and refine UIDB by user’s feedback information are discussed in detail in this paper. By means of UIDB, ECWS can actively recommend suitable series of pre‐sent web pages for different user groups and gradually arrive at their aim: offering personalized service for user groups.

The purpose of this paper is to propose a mobile and social-based question-and-answer (Q&A) system that analyzes users' social relationships and past answering behavior, considers users' interest similarity and answer quality to infer suitable respondents and forwards the questions to users that are willing to give high quality answers.

This research applies first-order logic (FOL) inference calculation to generate question/interest ID that combines a users' social information, interests and social network intimacy to choose the nodes that can provide high-quality answers. After receiving a question, a friend can answer it, forward it to their friends according to the number of TTL (Time-to-Live) hops, or send the answer directly to the server. This research collected data from the TripAdvisor.com website and uses it for the experiment. The authors also collected previously answered questions from TripAdvisor.com; thus, subsequent answers could be forwarded to a centralized server to improve the overall performance.

The authors have first noticed that even though the proposed system is decentralized, it can still accurately identify the appropriate respondents to provide high-quality answers. In addition, since this system can easily identify the best answerers, there is no need to implement broadcasting, thus reducing the overall execution time and network bandwidth required. Moreover, this system allows users to accurately and quickly obtain high-quality answers after comparing and calculating interest IDs. The system also encourages frequent communication and interaction among users. Lastly, the experiments demonstrate that this system achieves high accuracy, high recall rate, low overhead, low forwarding cost and low response rate in all scenarios.

This paper proposes a mobile and social-based Q&A system that applies FOL inference calculation to analyze users' social relationships and past answering behavior, considers users' interest similarity and answer quality to infer suitable respondents and forwards the questions to users that are willing to give high quality answers. The experiments demonstrate that this system achieves high accuracy, high recall rate, low overhead, low forwarding cost and low response rate in all scenarios.

Online businesses require a deep understanding of their customers’ interests to innovate and develop new products and services. Users, on the other hand, rarely express their interests explicitly. The purpose of this study is to predict users’ implicit interest in products of an online store based on their mouse behavior through various product page elements.

First, user mouse behavior data is collected throughout an online store website. Next, several mouse behavioral features on the product pages elements are extracted and finally, several models are extracted using machine learning techniques to predict a user’s interest in a product.

The results indicate that focusing on mouse behavior on various page elements improves user preference prediction accuracy compared to other available methods.

User mouse behavior was used to predict consumer preferences in this study, therefore gathering additional data on user demography, personality dimensions and emotions may significantly aid in accurate prediction.

Mouse behavior is the most repeated behavior during Web page browsing through personal computers and laptops. It has been referred to as implicit feedback in some studies and an effective way to ascertain user preference. In these studies, mouse behavior is only assessed throughout the entire Web page, lacking a focus on different page elements. It is assumed that in online stores, user interaction with key elements of a product page, such as an image gallery, user reviews, a description and features and specifications, can be highly informative and aid in determining the user’s interest in that product.

This paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users’ domain (s) of interest is a significant step towards addressing their domain-based trustworthiness through an accurate understanding of their content in their OSNs.

This study uses a Twitter mining approach for domain-based classification of users and their textual content. The proposed approach incorporates machine learning modules. The approach comprises two analysis phases: the time-aware semantic analysis of users’ historical content incorporating five commonly used machine learning classifiers. This framework classifies users into two main categories: politics-related and non-politics-related categories. In the second stage, the likelihood predictions obtained in the first phase will be used to predict the domain of future users’ tweets.

Experiments have been conducted to validate the mechanism proposed in the study framework, further supported by the excellent performance of the harnessed evaluation metrics. The experiments conducted verify the applicability of the framework to an effective domain-based classification for Twitter users and their content, as evident in the outstanding results of several performance evaluation metrics.

This study is limited to an on/off domain classification for content of OSNs. Hence, we have selected a politics domain because of Twitter’s popularity as an opulent source of political deliberations. Such data abundance facilitates data aggregation and improves the results of the data analysis. Furthermore, the currently implemented machine learning approaches assume that uncertainty and incompleteness do not affect the accuracy of the Twitter classification. In fact, data uncertainty and incompleteness may exist. In the future, the authors will formulate the data uncertainty and incompleteness into fuzzy numbers which can be used to address imprecise, uncertain and vague data.

This study proposes a practical framework comprising significant implications for a variety of business-related applications, such as the voice of customer/voice of market, recommendation systems, the discovery of domain-based influencers and opinion mining through tracking and simulation. In particular, the factual grasp of the domains of interest extracted at the user level or post level enhances the customer-to-business engagement. This contributes to an accurate analysis of customer reviews and opinions to improve brand loyalty, customer service, etc.

This paper fills a gap in the existing literature by presenting a consolidated framework for Twitter mining that aims to uncover the deficiency of the current state-of-the-art approaches to topic distillation and domain discovery. The overall approach is promising in the fortification of Twitter mining towards a better understanding of users’ domains of interest.

Can mobile instant messaging (MIM) make people entering into the state of cognitive absorption (CA)? The purpose of this paper is to investigate whether CA can help explain users’ satisfaction during the process of MIM, while interactivity and interest are operated as determinants of CA as well as directly associated with satisfaction.

This study proposes a satisfaction model that is adapted from the CA theory to investigate MIMs users’ satisfaction with two determinants, interactivity and interest. Specifically, CA is operated as a second-order formative construct with four reflective dimensions, including curiosity, focused immersion, heightened enjoyment and temporal dissociation. Partial least square structural equation modeling was applied to evaluate the causal links of the model with the data collected from 472 LINE users who all had long using experience.

The results showed that CA in MIM, fueled by interactivity and interest, is positively related to satisfaction. Interactivity and interest themselves were also significantly associated with satisfaction. Among them, interactivity has the most influence on satisfaction, followed by interest and CA. Surprised, curiosity and focused immersion did not formative CA in MIM.

The present study focuses on user satisfaction of a specific MIM (LINE) and collects data from users within a specific region (Taiwan). Other researchers must take these constrains into consideration when referencing this study.

To the best of the authors’ knowledge, this is the first study which confirmed that people still enter into the state of CA when using MIM on smartphone, even though the using environment is drastically different from that on desktop. It indicates that prior theories in CA with desktop-based software are still applicable and serve as a basis for more studies in the mobile context to a certain extent, but other factors should also be considered. As interactivity and interest are conducive to CA, leading to user satisfaction, an MIM app can be more popular if the two factors are incorporated.

The purpose of this article was to confirm whether users’ interests are reflected by tweeted Web pages, and to evaluate the credibility of interest reflection of tweeted Web pages.

Interest reflection of Twitter is investigated based on the context of sharing behavior. A context-oriented approach is proposed to evaluate the interest reflection of tweeted Web pages based on machine learning. Some different distribution models of similarity are present, and infer whether tweeted Web pages reflect respective users’ interests by analyzing user access profiles.

The analysis of browsing behaviors finds that many users partially hide their own concerns, hobbies and interests, and emphasize the concerns about social phenomenon. The extensive experimental results showed the context-oriented approach is effective on real net view data.

As the first-of-its-kind study on evaluating the credibility of interest reflection on Twitter, extensive experiments have been conducted on the data sets containing real net view data. For higher accuracy and less subjectivity, various features are generated from user’s Web view and Twitter submission background with some different context factors.

Recommender agents are used to make recommendations of interesting items in a wide variety of application domains, such as web page recommendation, music, e‐commerce, movie recommendation, tourism, restaurant recommendation, among others. Despite the various and different domains in which recommender agents are used and the variety of approaches they use to represent user interests and make recommendations, there is some functionality that is common to all of them, such as user model management and recommendation of interesting items. This paper aims at generalizing these common behaviors into a framework that enables developers to reuse recommender agents' main characteristics in their own developments.

This work presents a framework for recommendation that provides the control structures, the data structures and a set of algorithms and metrics for different recommendation methods. The proposed framework acts as the base design for recommender agents or applications that want to add the already modeled and implemented capabilities to their own functionality. In contrast with other proposals, this framework is designed to enable the integration of diverse user models, such as demographic, content‐based and item‐based. In addition to the different implementations provided for these components, new algorithms and user model representations can be easily added to the proposed approach. Thus, personal agents originally designed to assist a single user can reuse the behavior implemented in the framework to expand their recommendation strategies.

The paper describes three different recommender agents built by materializing the proposed framework: a movie recommender agent, a tourism recommender agent, and a web page recommender agent. Each agent uses a different recommendation approach. PersonalSearcher, an agent originally designed to suggest interesting web pages to a user, was extended to collaboratively assist a group of users using content‐based algorithms. MovieRecommender recommends interesting movies using an item‐based approach and Traveller suggests holiday packages using demographic user models. Findings encountered during the development of these agents and their empirical evaluation are described here.

The advantages of the proposed framework are twofold. On the one hand, the functionality provided by the framework enables the development of recommender agents without the need for implementing its whole set of capabilities from scratch. The main processes and data structures of recommender agents are already implemented. On the other hand, already existing agents can be enhanced by incorporating the functionality provided by the recommendation framework in order to act collaboratively.

This paper aims to propose an approach for the analysis of user interest based on tweets, which can be used in the design of user recommendation systems. The extract topics are seen positively by the user.

The proposed approach is based on the combination of sentiment extraction and classification analysis of tweet to extract the topic of interest. The proposed hybrid method is original. The topic extraction phase uses a method based on semantic distance in the WordNet taxonomy. Sentiment extraction uses NLPcore.

The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results and confirm the suitability of the approach combining sentiment and categorization for the topic of interest extraction.

The hybrid method combining sentiment extraction and classification for user positive topics represents a novel contribution with many potential applications.

The functionality of positive topic extraction is very useful as a component in the design of a recommender system based on user profiling from Twitter user behaviors.

The application of the proposed method in short-text social network can be massive and beyond the applications in tweets.

There are few works that have considered both sentiment analysis and classification to find out users’ interest. The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results.