Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents

Papagelis, Manos; Plexousakis, Dimitris

doi:10.1016/j.engappai.2005.06.010

Cited by 159 publications

(33 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a common evaluation practice in systems examined for other domains (e.g. Breese et al 1998;Herlocker et al 2002;Deshpande & Karypis 2004;Papagelis & Plexousakis 2005). Previous evaluation studies have clearly indicated that careful testing and parameterization has to be carried out before a recommender system is finally deployed in a real setting.…”

Section: Assessing Current Statusmentioning

confidence: 99%

Collaborative recommendation of e‐learning resources: an experimental investigation

Manouselis

Vuorikari²,

Assche³

2010

Computer Assisted Learning

View full text Add to dashboard Cite

Repositories with educational resources can support the formation of online learning communities by providing a platform for collaboration. Users (e.g. teachers, tutors and learners) access repositories, search for interesting resources to access and use, and in many cases, also exchange experiences and opinions. A particular class of online services that take advantage of the collected knowledge and experience of users are collaborative filtering ones. The successful operation of such services in the context of real-life applications requires careful testing and parameterization before their actual deployment. In this paper, the case of developing a learning resources' collaborative filtering service for an online community of teachers in Europe was examined. More specifically, a data set of evaluations of learning resources was collected from the teachers that use the European Schoolnet's learning resource portal. These evaluations were then used to support the experimental investigation of design choices for an online collaborative filtering service for the portal's learning resources. A candidate multi-attribute utility collaborative filtering algorithm was appropriately parameterized and tested for this purpose. Results indicated that the development of such systems should be taking place considering the particularities of the actual communities that are to be served.

show abstract

Section: Assessing Current Statusmentioning

confidence: 99%

Collaborative recommendation of e‐learning resources: an experimental investigation

Manouselis

Vuorikari²,

Assche³

2010

Computer Assisted Learning

View full text Add to dashboard Cite

show abstract

“…The present study of Papagel is and Plexousakis 18 showed that the item-based algorithm performed better than the user-based algorithm. Yu et al 19 found that the Pearson correlation coefficient outperformed the Kendall correlation and that positively correlated neighbors yielded higher accuracy.…”

Section: Techniques Used In Recommendation Systemsmentioning

confidence: 91%

“…The next phase compares the integrated method with other techniques by using the same dataset for both training and testing in each method. The techniques used in the comparison study are collaborative filtering 18 and association rules 20 .…”

Section: Experimental Designmentioning

confidence: 99%

A Framework for Integrated Mobile Content Recommendation

Paireekreng¹

2013

ijecs

View full text Add to dashboard Cite

Content filtering in a mobile recommendation system plays a vital role in providing solution to help mobile device users obtain their desire content. However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. These problems can be viewed as a user's first time connection to a mobile recommendation system and initial rating of the content in an early stage of the system. Hence, to obtain personalized content for mobile user, mobile content filtering is needed. This paper proposes a framework for integrated mobile content recommendation. The framework makes use of classification and adaptive association rule techniques to build an integrated model. The results demonstrate that the proposed framework outperforms related techniques. This can address the problem of sparsity for mobile content recommendation systems.

show abstract

“…• Item-KNN: Neighbourhood based approaches are the most popular and widely used along with MF techniques. Here, model seeks to find the items which are nearest neighbours to the items already purchased by the customers [27].…”

Section: Methods For Comparative Analysismentioning

confidence: 99%

A Product Recommendation Model Based on Recurrent Neural Network

Nelaturi¹,

Devi²

2019

JESA

View full text Add to dashboard Cite

In traditional recommender systems, the product recommendations are generally made based on the static behavior or preference of customers. This paper designs a novel production recommendation model that processes customer-product interaction data as a time-based sequential data, and makes personalized product recommendations based on the purchase patterns of customers. Specifically, the model relies on the deep learning technique of recurrent neural network (RNN) to uncover the dynamics in purchase patterns of customers; a bidirectional model with attention mechanism was introduced to personalize the product recommendations. The effectiveness of the proposed model was verified through an experiment on a benchmark dataset called Movie lens. The experimental results show that the RNN-based model can efficiently capture the temporal dynamics of customer preferences, and then generate highly individualized product recommendations.

show abstract

Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents

Cited by 159 publications

References 14 publications

Collaborative recommendation of e‐learning resources: an experimental investigation

Collaborative recommendation of e‐learning resources: an experimental investigation

A Framework for Integrated Mobile Content Recommendation

A Product Recommendation Model Based on Recurrent Neural Network

Contact Info

Product

Resources

About