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Article
Publication date: 1 February 2004

Xue‐Feng Jiang

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…

Abstract

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.

Details

Kybernetes, vol. 33 no. 2
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 12 June 2020

Senthilkumar N C and Pradeep Reddy Ch

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

Abstract

Purpose

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

Design/methodology/approach

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

Findings

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

Research limitations/implications

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

Practical implications

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

Social implications

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.

Originality/value

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

Details

International Journal of Intelligent Unmanned Systems, vol. 8 no. 4
Type: Research Article
ISSN: 2049-6427

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Article
Publication date: 1 February 2004

Ming‐Hua Yang

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…

Abstract

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.

Details

Kybernetes, vol. 33 no. 2
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 6 March 2018

Bilal Abu-Salih, Pornpit Wongthongtham and Chan Yan Kit

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Details

Journal of Knowledge Management, vol. 22 no. 5
Type: Research Article
ISSN: 1367-3270

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Article
Publication date: 4 December 2018

Avus C.Y. Hou, Wen-Lung Shiau and Rong-An Shang

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

Industrial Management & Data Systems, vol. 119 no. 4
Type: Research Article
ISSN: 0263-5577

Keywords

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Article
Publication date: 11 November 2014

Hao Han, Hidekazu Nakawatase and Keizo Oyama

The purpose of this article was to confirm whether usersinterests are reflected by tweeted Web pages, and to evaluate the credibility of interest reflection of tweeted…

Abstract

Purpose

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

Design/methodology/approach

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 usersinterests by analyzing user access profiles.

Findings

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.

Originality/value

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.

Details

International Journal of Web Information Systems, vol. 10 no. 4
Type: Research Article
ISSN: 1744-0084

Keywords

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Article
Publication date: 10 November 2020

Samira Khodabandehlou, S. Alireza Hashemi Golpayegani and Mahmoud Zivari Rahman

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start…

Abstract

Purpose

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

Design/methodology/approach

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

Findings

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

Research limitations/implications

The research data were limited to only one e-clothing store.

Practical implications

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

Originality/value

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.

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Article
Publication date: 2 February 2010

Daniela Godoy, Silvia Schiaffino and Analía Amandi

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Internet Research, vol. 20 no. 1
Type: Research Article
ISSN: 1066-2243

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Article
Publication date: 16 April 2018

Alfredo Milani, Niyogi Rajdeep, Nimita Mangal, Rajat Kumar Mudgal and Valentina Franzoni

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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

Practical implications

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.

Social implications

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

Originality/value

There are few works that have considered both sentiment analysis and classification to find out usersinterest. 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.

Details

International Journal of Web Information Systems, vol. 14 no. 1
Type: Research Article
ISSN: 1744-0084

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Article
Publication date: 30 March 2012

Clemens Schefels and Roberto V. Zicari

An important issue in the management of a web‐based user community, where users are registered to a web portal, is to identify patterns of users' interest. In this…

Abstract

Purpose

An important issue in the management of a web‐based user community, where users are registered to a web portal, is to identify patterns of users' interest. In this context, the users' feedback plays a major role. The purpose of this paper is to define a novel framework analysis for managing the feedback given by registered visitors of a web site.

Design/methodology/approach

The paper presents a new technique to integrate the feedback explicitly given by users into already existing user profiles. The authors introduce the novel concepts of scope, filtering, and relevance profiles for managing users' feedback. The new concept of Relevance Profile (RP) is defined.

Findings

Using the framework, the authors were able to discover patterns of usage of registered users of a web site.

Practical implications

The practical applicability of the approach is validated by a use case study showing how the framework can be used with a real web site. The authors used Gugubarra as a reference system, a prototype for creating and managing web user profiles, developed by the DBIS group at the Goethe‐University of Frankfurt.

Originality/value

A new way to integrate the user feedback into interest profiles and a novel framework to analyze and discover patterns of interests are presented. The paper is an extended version (more than 50 per cent novel material) of a previous paper presented at the iiWAS2010 conference.

Details

International Journal of Web Information Systems, vol. 8 no. 1
Type: Research Article
ISSN: 1744-0084

Keywords

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