A Collective Opinion Mechanism for Crowdfunding Recommendation

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Jin, Bih-Huang; Li, Yung-Ming; Kamolporn, Sanamlao

Abstract: ... Expand

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Topics: crowdsourcing crowdfunding Twitter social network Python

Methods: sentiment analysis experiment topic model logistic regression parametric test

An empirical study of content-based recommendation systems in mobile app markets

2023 | Decision Support Systems | Citations: 0

Authors: Jozani, Mohsen; Liu, Charles Zhechao; Choo, Kim-Kwang Raymond

Abstract: Recommendation systems are widely used to promote product visibility and sales. ... Expand

Abstract: Recommendation systems are widely used to promote product visibility and sales. However, past research suggests that they primarily benefit market superstars and therefore, can be detrimental to niche products. This is especially critical in superstar dominated markets such as the market of mobile apps. This study incorporates social network analysis and econometric models to empirically examine the impact of content-based filtering (CBF) recommendation systems on the distribution of demand in mobile app markets. The analysis results of two comprehensive panel datasets from App Store and Google Play suggest that CBF recommendations favor niche items and effectively boost the long tail of the market. Moreover, consistent with signaling theory, relative quality signals provided by recommendation systems can impact consumer decision making process, leading to a spillover of demand. Our findings provide important implications for developers and market operators to better promote their products in the highly competitive market of mobile apps. Collapse

Topics: mobile application mobile application market digital distribution recommender system Google+

Methods: computational algorithm robustness check instrumental variables estimation longitudinal research statistical hypothesis test

Theories: signaling theory

Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multiarmed Bandit Approach

2023 | Information Systems Research | Citations: 3

Authors: Zhou, Tongxin; Wang, Yingfei; Yan, Lu (Lucy); Tan, Yong

Abstract: Online healthcare platforms provide users with various intervention programs to ... Expand

Abstract: Online healthcare platforms provide users with various intervention programs to promote personal wellness. Given the many options available, it’s often difficult for individuals to decide in which intervention to participate, especially when they lack the experience or knowledge to evaluate the interventions. This may discourage individuals’ continuous engagement in online health management. In this study, we are motivated to develop a personalized healthcare recommendation framework to help individuals better discover the interventions that fit their needs. Considering the challenges in intervention adaptation and diversification in a highly dynamic online healthcare environment, we propose an innovative online learning framework that synthesizes deep representation learning and a theory-guided diversity promotion scheme. We evaluate our approach through a real-world data set on users’ intervention participation in an online weight-loss community. Our results provide strong evidence for the effectiveness of our proposed recommendation framework and each of its design components. Our study contributes to the emerging information systems research on prescriptive analytics and the application of business intelligence. The proposed modeling framework and evaluation results offer important implications for multiple stakeholders, including online healthcare platforms, policymakers, and users. History: Ahmed Abbasi, Senior Editor; Huimin Zhao, Associate Editor. Funding: This work was supported in part by the National Natural Science Foundation of China [Grant 71729001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1191. Collapse

Topics: recommender system logistics management online learning system collaborative filtering usability

Methods: deep learning computational algorithm artificial neural network experiment design artifact

Theories: social cognitive theory

Leveraging Trust Relations to Improve Academic Patent Recommendation

2022 | HICSS | Citations: 0

Authors: Chen, Yuwen; Ma, Jian; Zhu, Peihu; Huang, Xiaoming; Jin, Qin

Abstract: Academic patent trading is one of the important ways for university technology t ... Expand

Abstract: Academic patent trading is one of the important ways for university technology transfer. Compared to industry patent trading, academic patent trading suffers from a more serious information asymmetric problem. It needs a recommendation service to help companies identify academic patents that they want to pay. However, existing recommendation approaches have limitations in facilitating academic patent trading in online patent platforms because most of them only consider patent-level characteristics. A high trust degree of a company towards academic patents can alleviate the information asymmetry and encourage trading. This study proposes a novel academic patent recommendation approach with a hybrid strategy, combining citation-based relevance, connectivity, and trustworthiness. An offline experiment is conducted to evaluate the performance of the proposed recommendation approach. The results show that the proposed method performs better than the baseline methods in both accuracy and ranking. Collapse

Topics: recommender system logistics management electronic market content-based filtering database system

Methods: experiment co-citation analysis regression analysis method machine learning literature study

A Recommender System for Healthy Food Choices: Building a Hybrid Model for Recipe Recommendations using Big Data Sets

2021 | HICSS | Citations: 0

Authors: Chavan, Pallavi; Thoms, Brian; Isaacs, Jason

Abstract: Advances in Big Data analytics and machine learning have offered intangible bene ... Expand

Abstract: Advances in Big Data analytics and machine learning have offered intangible benefits across many areas of one’s health. One such area is a move towards healthier lifestyle choices such as one’s diet. Recommender systems apply techniques that can filter information and narrow that information down based on user preferences or user needs and help users choose what information is relevant. Commonly adopted across e-commerce sites, social networking and entertainment industries, recommender systems can also support nutrition-based health management, offering individuals more food options, not only based on one’s preferred tastes but also on one’s dietary needs and restrictions. This research presents the design, implementation and evaluation of three recommender systems using content-based, collaborative filtering and hybrid recommendation models within the nutrition domain. Collapse

Topics: collaborative filtering recommender system internet technology big data content-based filtering

Methods: computational algorithm data transformation principal component analysis Cross-Industry Standard Process for Data Mining NLTK

Towards the Use of Clustering Algorithms in Recommender Systems

2020 | Americas Conference on Information Systems | Citations: 0

Authors: Miranda, Leandro; Viterbo, Jose; Bernardini, Flávia

Abstract: Recommender Systems have been intensively used in Information Systems in the la ... Expand

Abstract: Recommender Systems have been intensively used in Information Systems in the last decades, facilitating the choice of items individually for each user based on your historical. Clustering techniques have been frequently used in commercial and scientific domains in data mining tasks and visualization tools. However, there is a lack of secondary studies in the literature that analyze the use of clustering algorithms in Recommender Systems and their behavior in different aspects. In this work, we present a Systematic Literature Review (SLR), which discusses the different types of information systems with the use of the clustering algorithm in Recommender Systems, which typically involves three main recommendation approaches found in literature: collaborative filtering, content-based filtering, and hybrid recommendation. In the end, we did a quantitative analysis using K-means clustering for finding patterns between clustering algorithms, recommendation approaches, and some datasets used in their publications. Collapse

Topics: collaborative filtering recommender system database system content-based filtering IT project

Methods: cluster analysis experiment computational algorithm structured literature research design methodology

DO USERS ALWAYS WANT TO KNOW MORE? INVESTIGATING THE RELATIONSHIP BETWEEN SYSTEM TRANSPARENCY AND USERS’ TRUST IN ADVICE-GIVING SYSTEMS

2019 | European Conference On Information Systems | Citations: 0

Authors: Zhao, Ruijing; Benbasat, Izak; Cavusoglu, Hasan

Abstract: Users' adoptions of online-shopping advice-giving systems (AGSs) are crucial fo ... Expand

Abstract: Users' adoptions of online-shopping advice-giving systems (AGSs) are crucial for e-commerce websites to attract users and increase profits. Users' trust in AGSs influences them to adopt AGSs. While previous studies have demonstrated that AGS transparency increases users' trust through enhancing users' understanding of AGSs' reasoning, hardly any attention has been paid to the possible inconsistency between the level of AGS transparency and the extent to which users feel they understand the logic of AGSs' inner working. We argue that the relationship between them may not always be positive. Specifically, we posit that providing information regarding how AGSs work can enhance users' trust only when users have enough time and ability to process and understand the information. Moreover, providing excessively detailed information may even reduce users' perceived understanding of AGSs, and thus hurt users' trust. In this research, we will use a lab experiment to explore how providing information with different levels of detail will influence users' perceived understanding of and trust in AGSs. Our study would contribute to the literature by exploring the potential inverted U-shape relationship among AGS transparency, users' perceived understanding of and trust in AGSs, and contribute to the practice by offering suggestions for designing trustworthy AGSs. Collapse

Topics: collaborative filtering IT work content-based filtering website

Methods: laboratory experiment experiment regression analysis method survey experimental design

Recommender Systems in Online Platforms for Social Campaigns

2018 | International Conference on Information Systems | Citations: 0

Authors: Elnoshokaty, Ahmed; Wang, Yi; Deng, Shuyuan; Meservy, Thomas; Elsaied, Mohamed; Peer, Firaz; El-Gayar, Omar

Abstract: Online social change campaigns have recently become important channels of civic ... Expand

Abstract: Online social change campaigns have recently become important channels of civic participation. However, most online change platforms tend to use simple indicators (such as popularity) to rank social campaigns, hence creating a situation where the most popular campaigns dominate the rankings and attract the most attention. Online petitions which focus on niche issues are often ranked lower below these more popular campaigns. For example, a petition for an environmental issue in a small town may not even be seen because it is just not popular enough with the majority of users on the platform. The simple ranking mechanism used by most online change platforms cannot effectively link petitions with those who are genuinely concerned with them. At most, less than 10% of online petitions get the chance to fulfill their causes. Collapse

Topics: recommender system Twitter collaborative filtering crowdsourcing social network

Methods: survey experiment topic model field experiment latent dirichlet allocation

Process Modeling Recommender Systems: A Generic Data Model and Its Application to a Smart Glasses-based Modeling Environment

2018 | Business & Information Systems Engineering | Citations: 0

Authors: Fellmann, Michael; Metzger, Dirk; Jannaber, Sven; Zarvic, Novica; Thomas, Oliver

Abstract: The manual construction of business process models is a time-consuming, error-p ... Expand

Abstract: The manual construction of business process models is a time-consuming, error-prone task and presents an obstacle to business agility. To facilitate the construction of such models, several modeling support techniques have been suggested. However, while recommendation systems are widely used, e.g., in e-commerce, these techniques are rarely implemented in process modeling tools. The creation of such systems is a complex task since a large number of requirements and parameters have to be taken into account. In order to improve the situation, the authors have developed a data model that can serve as a backbone for the development of process modeling recommender systems (PMRS). This article outlines the systematic development of this model in a stepwise approach using established requirements and validates it against a data model that has been reverse-engineered from a realworld system. In a last step, the paper illustrates an exemplary instantiation of the data model in a Smart Glasses-based modeling environment and discusses business process agility issues. The authors expect their contribution to provide a useful starting point for designing the data perspective of process modeling recommendation features that support business agility in process-intensive environments. Collapse

Topics: recommender system content-based filtering collaborative filtering user requirement smart glass

Methods: data modeling design artifact case study survey structural equation modeling

Web Personalization Cues and Their Differential Effects on User Assessments of Website Value

2015 | Journal of Management Information Systems | Citations: 97

Authors: Benlian, Alexander

Abstract: Although various kinds of personalization cues are pervasively used on websites, ... Expand

Abstract: Although various kinds of personalization cues are pervasively used on websites, previous research studies have treated web personalization primarily as a coarse-grained, monolithic block (e.g., by comparing personalization vs. nonpersonalization or personalization vs. privacy) rather than as a combination of salient types of personalization cues that may create—either jointly or separately—different effects on user assessments of website value. Based on the stimulus–organism–response framework, we develop a research model that proposes users’ preference fit and perceived enjoyment as two key intervening mechanisms that carry over the differential effects of content and design personalization cues on users’ willingness to stick to a website and to pay for website offerings. In a field experiment with 206 subjects using a real-life news aggregator website, our findings provide evidence in support of different effect paths emanating from content and design personalization cues. Furthermore, we show that the effects of content personalization cues on website stickiness and users’ willingness to pay (WTP) are mediated by both preference fit and perceived enjoyment, whereas design personalization cues exert their effects on website stickiness only through perceived enjoyment. Counter to intuition, we find that a combination of content and design personalization cues is ineffective—or even counterproductive—in increasing preference fit and users’ WTP above and beyond the levels generated by content cues alone. With regard to perceived enjoyment and website stickiness, however, content and design personalization cues exhibit synergistic properties indicating that the combination of both cues are more than the sum of the individual cues alone. Recommendations are provided as to how online managers and web designers can use web personalization cues to positively influence website stickiness and to strengthen their digital business model. Collapse

Topics: usability personalization website digital news website design

Methods: survey experiment partial least squares regression parametric test ANOVA

Theories: theory of task technology fit