Recommender Systems for the Web

Schafer, J. Ben; Konstan, Joseph A.; Riedl, John

doi:10.1007/1-84628-290-x_6

Cited by 12 publications

(10 citation statements)

References 43 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are some limitations of this framework and the resulting instantiation of the ROLS. First, as in any collaborative filtered RS, there may not be adequate data to derive recommendations at the start of system‐use, known as a “cold start” in recommender systems (Schafer, Konstan, & Riedl, ; Tran, ). Second, recommendations will need continuous checking and refinement to make sure that they continue to meet learners’ needs.…”

Section: Discussionmentioning

confidence: 99%

Evaluating a Prototype of a Recommender‐Driven Online Learning System

Peiris

Gallupe

2018

Decision Sci J Innov Edu

View full text Add to dashboard Cite

Recommender‐driven online learning systems (ROLS) are at the forefront of new computer‐based learning. They incorporate machine learning to allow learning‐by‐doing, generating personalized recommendations in the process. This article describes the evaluations of a new type of online learning systems, ROLS. This evaluation was carried out in three phases using a design science research approach. In Phase I, building the ROLS prototype validated the conceptual framework used. In Phase II, building an instantiation of ROLS to teach Structured Query Language (SQL), SQL‐with‐Ease, validated the ROLS prototype. In Phase III, a laboratory experiment evaluated learning outcomes from using SQL‐with‐Ease compared with two other traditional forms of learning. A set of qualitative interviews carried out with learners soon after using the system confirmed that the system was effective. They indicated that more work on fine‐tuning recommendations generated by the system could further improve learner satisfaction. The key implication for practitioners is that ROLS have the potential to improve learning outcomes significantly. Implications for researchers are that evaluations of ROLS, which include formative and summative evaluations, are essential to improve their performance and that developing innovative approaches to evaluation can advance these learning technologies.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluating a Prototype of a Recommender‐Driven Online Learning System

Peiris

Gallupe

2018

Decision Sci J Innov Edu

View full text Add to dashboard Cite

show abstract

“…In this way, diverse services and applications will be moved on. In the future, Mobile commerce is expected to be as popular as e-commerce [22].…”

Section: Discussionmentioning

confidence: 99%

A Novel RSRM Algorithm for Mining services for Better Enhancement in Small HandHeld Devices

Kushwaha¹,

Haneef²

2011

IJCA

View full text Add to dashboard Cite

This paper presents a case study on data mining services able to support decision makers in strategic planning for the enhancement of small handheld devices. The application provides e-Knowledge services for the analysis of territorial dynamics by processing and modeling huge amount of data, in order to discover rules and patterns in a distributed and heterogeneous content environment. For the analysis of structured data, the application covers the whole Knowledge Discovery process. The purpose of the paper is to show how to implement existing techniques in a flexible architecture for providing new added value services. Finally in our paper, we proposed a RSRM( Read Subset Removal Miner) algorithm for mining on mobile devices.

show abstract

“…The way inanimate websites communicate with their users was changed by recommender systems. For example, instead of letting the users search for and potentially buy products, recommender systems increase interaction to provide a richer experience …”

Section: Background On Recommender Systemsmentioning

confidence: 99%

Time‐aware service recommendation: Taxonomy, review, and challenges

Mezni

Fayala²

2018

Softw Pract Exp

View full text Add to dashboard Cite

Summary Nowadays, a huge number of available Web services offer the same functionalities and a high quality of service, which makes the selection of suitable services a difficult task. In such situation, the services must be differentiated by additional criteria such as users' ratings. To meet this goal, recommendation techniques become a natural choice to cope with the challenging task of optimal service selection and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation are static, whereas in the real world, the perception and popularity of Web services may continually change, and users' preferences and habits also shift frequently. Time is becoming an increasingly important factor in recommender systems, since time effects influence users' preferences to a large extent. In addition, quality‐of‐service performance of Web services is strongly linked to the service status and network environments, which are variable against time. Recently, a wide range of service recommendation approaches, dealing with the time dimension in user modeling and recommendation strategies, have been proposed. Thus, the purpose of this survey is to present a comprehensive study and analysis of the state‐of‐the‐art on time‐aware service recommendation. We identify the techniques used in recommender systems to provide the best services. Moreover, we present a classification of time‐aware recommender systems based on the target recommendation time, the type of relationship between users, and the type of feedback. Besides, we present a comparison between time‐aware recommendation approaches, and we discuss their advantages and disadvantages. Finally, challenges and requirements of time‐aware service recommendation as well as the future directions are identified according to the studied approaches.

show abstract

Recommender Systems for the Web

Cited by 12 publications

References 43 publications

Evaluating a Prototype of a Recommender‐Driven Online Learning System

Evaluating a Prototype of a Recommender‐Driven Online Learning System

A Novel RSRM Algorithm for Mining services for Better Enhancement in Small HandHeld Devices

Time‐aware service recommendation: Taxonomy, review, and challenges

Contact Info

Product

Resources

About