Hybreed: A software framework for developing context-aware hybrid recommender systems

Hussein, Tim; Linder, Timm; Gaulke, Werner; Ziegler, Jürgen

doi:10.1007/s11257-012-9134-z

Cited by 55 publications

(28 citation statements)

References 61 publications

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“…On that basis, Wu et al [33] changed the equilibrium factor into dynamic, but the balance factor was not significant. Hussein et al [34] provided a range of recommendation algorithms and strategies for producing group recommendations, as well as templates for combining different methods into hybrid recommenders. We find most research was based on two algorithms: CBF and CF.…”

Section: The Recommendation System and Hybrid Algorithmmentioning

confidence: 99%

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

Guo

Wang

2017

Symmetry

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Abstract:With the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system using the hybrid algorithm model. The proposed model first uses various recommendation algorithms to get a list of original recommendation results. Combined with the customer's feedback in an interactive manner, it then establishes the weights of corresponding recommendation algorithms. Finally, the synthetic formula of evidence theory is used to fuse the original results to obtain the final recommendation products. The recommendation performance of the proposed method is compared with that of traditional methods. The results of the experimental study through a Taobao online dress shop clearly show that the proposed method increases the efficiency of data mining in the consumer coverage, the consumer discovery accuracy and the recommendation recall. The hybrid recommendation algorithm complements the advantages of the existing recommendation algorithms in data mining. The interactive assigned-weight method meets consumer demand better and solves the problem of information overload. Meanwhile, our study offers important implications for e-commerce platform providers regarding the design of product recommendation systems.

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Section: The Recommendation System and Hybrid Algorithmmentioning

confidence: 99%

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

Guo

Wang

2017

Symmetry

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“…Finally, the paper "Hybreed: A Software Framework for Developing ContextAware Hybrid Recommender Systems" (Hussein et al 2014) by Tim Hussein, Timm Linder, Werner Gaulke, and Jürgen Ziegler addresses the systems engineering perspective and introduces a software framework for the development of context-aware and hybrid recommenders. Despite the high practical relevance of recommender systems in industry, little research on engineering aspects of such systems has been done, and no comprehensive software framework is yet available that supports componentbased development approaches for such complex systems.…”

Section: Papers In This Issuementioning

confidence: 99%

Preface to the special issue on context-aware recommender systems

Adomavicius

Jannach

2013

User Model User-Adap Inter

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Context-aware recommender systemsRecommender systems represent a popular area of personalization technologies that has enjoyed a tremendous amount of research and development activity in both academia and industry in the last 10-15 years. Recommender systems research typically explores and develops techniques and applications for recommending various products or services to individual users based on the knowledge of users' tastes and preferences as well as users' past activities (such as previous purchases), which are applicable in a variety of domains and settings (Jannach et al. 2010).While a substantial amount of research has already been performed in the area of recommender systems, many existing approaches have focused on recommending the most relevant items to users and do not take into account any additional contextual information, such as time, location, weather, the user's current goals, the user's mood, presence of other people, or the type of the device through which the recommendation is consumed. In other words, traditionally recommender systems deal with applications having only two types of entities, users and items, and do not put them into a context when providing recommendations. However, the importance of contextual information has been recognized by researchers and practitioners in many

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“…Moreover, while (Francis Jr, 2000;Hussein, 2013;Kovacs and Ueno, 2006) represent the domain knowledge and context factors even in sparse data, and extend spreading activation network with link types and context nodes to generate contextaware recommendations, they do not account user confidence and the sibling effect.…”

Section: Introductionmentioning

confidence: 99%

A Context-Aware Tourism Recommender System Based on a Spreading Activation Method

Bahramian

Abbaspour

Claramunt

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Users planning a trip to a given destination often search for the most appropriate points of interest location, this being a non-straightforward task as the range of information available is very large and not very well structured. The research presented by this paper introduces a context-aware tourism recommender system that overcomes the information overload problem by providing personalized recommendations based on the user's preferences. It also incorporates contextual information to improve the recommendation process. As previous context-aware tourism recommender systems suffer from a lack of formal definition to represent contextual information and user's preferences, the proposed system is enhanced using an ontology approach. We also apply a spreading activation technique to contextualize user preferences and learn the user profile dynamically according to the user's feedback. The proposed method assigns more effect in the spreading process for nodes which their preference values are assigned directly by the user. The results show the overall performance of the proposed context-aware tourism recommender systems by an experimental application to the city of Tehran.

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Hybreed: A software framework for developing context-aware hybrid recommender systems

Cited by 55 publications

References 61 publications

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

Preface to the special issue on context-aware recommender systems

A Context-Aware Tourism Recommender System Based on a Spreading Activation Method

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