Formation and Action of a Learning Community with Collaborative Learning Software

2023 | Journal of Management Information Systems | Citations: 0

Authors: Eryilmaz, Evren; Thoms, Brian; Ahmed, Zafor; Lee, Howard

Abstract: This paper explores the formation of a learning community facilitated by custom ... Expand

Abstract: This paper explores the formation of a learning community facilitated by custom collaborative learning software. Drawing on research in group cognition, knowledge building discourse, and learning analytics, we conducted a mixed-methods field study involving an asynchronous online discussion consisting of 259 messages posted by 50 participants. The cluster analysis results provide evidence that the recommender system within the software can support the formation of a learning community with a small peripheral cluster. Regarding knowledge building discourse, we identified the distinct roles of central, intermediate (i.e., middle of three clusters), and peripheral clusters within a learning community. Furthermore, we found that message lexical complexity does not correlate to the stages of knowledge building. Overall, this study contributes to the group cognition theory to deepen our understanding about collaboration to construct new knowledge in online discussions. Moreover, we add a much-needed text mining perspective to the qualitative interaction analysis model. Collapse

Topics: recommender system collaborative filtering pandemic online learning system open source

Methods: qualitative content analysis descriptive statistic sociometry synthesis mixed method

Theories: cognitive theory

Navigating Workload Compatibility Between a Recommender System and a NoSQL Database: An Interactive Tutorial

2023 | Communications of the Association for Information Systems | Citations: 0

Authors: Kayhan, Varol; Berndt, Donald

Abstract: Follow this and additional works at: https://aisel.aisnet.org/cais Recommended ... Expand

Abstract: Follow this and additional works at: https://aisel.aisnet.org/cais Recommended Citation Recommended Citation Abstract:In this tutorial, the issue of compatibility between a big data storage technology and an analytic workload is explored using a fictitious streaming company as an example. The tutorial offers an interactive approach to help students understand the importance of considering workload compatibility when adopting new technologies. We provide instructors with the following: two Jupyter Notebooks that analyze compatibility, a detailed instructor guide on how to execute these notebooks, lessons learned, and appendices containing solutions and explanations. This tutorial provides a valuable resource for instructors teaching courses in database systems, big data, and analytic concepts, helping students develop practical skills to navigate effectively the complexities of big data technologies. Collapse

Topics: database system document database recommender system collaborative filtering nosql database

Methods: database modeling design science survey

A deep multi-embedding model for mobile application recommendation

2023 | Decision Support Systems | Citations: 0

Authors: Liu, Yi-Hung; Chen, Yen-Liang; Chang, Po-Ya

Abstract: With the explosion in the number of mobile applications, it becomes difficult an ... Expand

Abstract: With the explosion in the number of mobile applications, it becomes difficult and time-consuming for users to find the most suitable and interesting application from the millions of applications that exist today. Therefore, mobile app recommendation becomes a top priority to help people solve this problem. In previous studies, there are two common methods to solve this problem, one is to apply matrix factorization to the rating data to predict the target user's ratings for other applications, and the other is to predict ratings based on the user's review text. In addition to combining the above two traditional methods, this study also combines deep learning methods to generate better models. For rating data, we use a deep matrix factorization method to obtain latent factor vectors of users and items from the rating matrix to improve the embedding of ratings. For review text, we improve the text representation by using a multi-embedding approach. Most importantly, our model also incorporates deep neural networks with nonlinear transformation capabilities to extract more representative abstract features. We conduct experiments on datasets collected from the real world. Extensive experimental results show that our proposed method has better performance than other existing methods. Collapse

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

Methods: BERT deep learning experiment sensitivity analysis artificial neural network

The Decoy Effect and Recommendation Systems

2023 | Information Systems Research | Citations: 0

Authors: Mousavi, Nasim; Adamopoulos, Panagiotis; Bockstedt, Jesse

Abstract: In this paper, we explore the decoy effect in recommendation systems. Including ... Expand

Abstract: In this paper, we explore the decoy effect in recommendation systems. Including a decoy item in a set of alternatives can influence the attractiveness of the other items by facilitating decision making. Prior research literature has indeed shown the decoy effect to be robust in traditional choice settings, with consistent reporting of an overall positive impact. Practitioners often use decoys to help drive demand for specific items. Recommendation systems too are increasingly being used to present item choice sets to customers and users. Both recommendation systems and the decoy effect can be used as strategies to help facilitate decision making. However, previous work has not examined the decoy effect in the context of recommendations. The decoy effect may facilitate consumer decision making and positively impact user behavior when used with recommendation systems. However, in the recommendation context, customers often have different expectations for the reliability and quality of the presented information. Hence, a decoy as a recommendation could signal issues in system reliability, resulting in a negative effect. We perform a randomized, controlled laboratory experiment and use persuasion theory as the theoretical lens to demonstrate that the decoy effect works differently in the context of recommendation systems. Specifically, we show that depending on the recommendation context, the decoy effect can or cannot drive demand for target items. We find that including a decoy minimizes the demand for the target option when personalized recommendations are presented, which deviates from the traditional decoy effect. However, a decoy increases the target’s demand when nonpersonalized recommendations are shown, following the conventional decoy effect. We explore the mechanism behind these findings and show the robustness of our results by conducting multiple analyses and additional experiments. The findings of our paper have important implications for the design of recommendation systems and our understanding of consumer decision making. History: Ravi Bapna, Senior Editor; Idris Adjerid, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1197. Collapse

Topics: recommender system usability personalization database system user behavior

Methods: experiment experimental group laboratory experiment computational algorithm experimental task

Recommendation Systems with Causal Inference-based Regularization

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Padalkar, Nakul; Lima, Victor De

Abstract: Recommender systems have become integral to modern society, facilitating person ... Expand

Abstract: Recommender systems have become integral to modern society, facilitating personalized experiences across various platforms by allowing the offering of tailored content, products, and services. Current approaches for building such systems employ neural networks that overfit the training data and fail to generalize when tested on previously unseen data. Furthermore, recommender systems are fundamentally causal inference problems since they seek to forecast conditional relationships that describe a user's response to specific advertisements or products. In this paper, we comprehensively examine the matrix factorization model for recommendation systems using the MovieLens 25M dataset. We demonstrate the limitations of standard collaborative filtering and motivate the need for more suitable regularization. This research lays the foundation for future work incorporating causal inference into recommendation systems, which has garnered increasing support in recent literature for its potential to improve model performance. Collapse

Topics: recommender system collaborative filtering

Methods: computational algorithm optimization model experiment PyTorch literature study

Understanding Information-Limiting Environments in Personalized News Platforms—A Systems Perspective

2023 | International Conference on Information Systems | Citations: 0

Authors: Schmalenbach, Kian; Brechtelsbauer, Bastian; Schötteler, Sebastian; Laumer, Sven

Abstract: ... Expand

Abstract: Collapse

Topics: user attitude collaborative filtering digital platform Facebook Twitter

Methods: experiment conceptual modelling design artifact computational algorithm theory development

Don’t Need All Eggs in One Basket: Reconstructing Composite Embeddings of Customers from Individual-Domain Embeddings

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Unger, Moshe; Li, Pan; Sen, Sahana (Shahana); Tuzhilin, Alexander

Abstract: Although building a 360-degree comprehensive view of a customer has been a long- ... Expand

Abstract: Although building a 360-degree comprehensive view of a customer has been a long-standing goal in marketing, this challenge has not been successfully addressed in many marketing applications because fractured customer data stored across different “silos” are hard to integrate under “one roof” for several reasons. Instead of integrating customer data, in this article we propose to integrate several domain-specific partial customer views into one consolidated or composite customer profile using a Deep Learning-based method that is theoretically grounded in Kolmogorov’s Mapping Neural Network Existence Theorem. Furthermore, our method needs to securely access domain-specific or siloed customer data only once for building the initial customer embeddings. We conduct extensive studies on two industrial applications to demonstrate that our method effectively reconstructs stable composite customer embeddings that constitute strong approximations of the ground-truth composite embeddings obtained from integrating the siloed raw customer data. Moreover, we show that these data-security preserving reconstructed composite embeddings not only perform as well as the original ground-truth embeddings but significantly outperform partial embeddings and state-of-the-art baselines in recommendation and consumer preference prediction tasks. Collapse

Topics: recommender system data security collaborative filtering marketing management Google+

Methods: BERT artificial neural network survey experiment machine learning

SocioLink: Leveraging Relational Information in Knowledge Graphs for Startup Recommendations

2023 | Journal of Management Information Systems | Citations: 0

Authors: Xu, Ruiyun; Chen, Hailiang; Zhao, J. Leon

Abstract: While venture capital firms are increasingly relying on recommendation models in ... Expand

Abstract: While venture capital firms are increasingly relying on recommendation models in investment decisions, existing startup recommendation models fail to consider the uniqueness of venture capital context, including two-sided matching between investing and investee firms and a lack of information disclosure requirements on startups. Following the design science research paradigm and guided by the proximity principle from social psychology, we develop a novel framework called SocioLink by depicting and analyzing various relations in a knowledge graph via machine learning. Our experimental results show that SocioLink significantly outperforms state-of-the-art startup recommendation methods in both accuracy and quality. This improvement is driven by not only the inclusion of social relations but also the superiority of modelling relations via knowledge graph. We also develop a web-based prototype to demonstrate explainable artificial intelligence. This work contributes to the FinTech literature by adding an innovative design artifact—SocioLink—for decision support in the investment context. Collapse

Topics: electronic finance recommender system collaborative filtering database system mergers and acquisition

Methods: experiment design artifact computational algorithm machine learning design science

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

Exploiting Linked Open Data for a Collaborative Filtering Recommendation System

2023 | Americas Conference on Information Systems | Citations: 0

Authors: de Souza, Paulo; Durao, Frederico; Almeida, João Paulo Dias de; Mascarenhas, Ramon; Oliveira, Mayki Dos Santos

Abstract: Recommendation Systems (RS) tackle Information Overload on the web, and Collabo ... Expand

Abstract: Recommendation Systems (RS) tackle Information Overload on the web, and Collaborative Filtering (CF) is a widely used technique to estimate content relevance for users based on similar profiles. Traditional CF, however, often overlooks hidden semantic information and item relationships. This work proposes an RS that integrates Linked Open Data with CF to improve recommendation precision by leveraging semantic information about resources and their relationships. The approach calculates semantic similarity among resources using the Linked Open Data graph. By comparing error rates (MAE and RMSE) in varying scenarios, the proposed approach shows at least a 1.5% improvement in precision rates compared to traditional CF. This study contributes insights for researchers seeking to exploit Linked Open Data in recommendation contexts. Collapse

Topics: collaborative filtering open data recommender system database system knowledge representation

Methods: computational algorithm k-nearest neighbor DBpedia experiment literature study