Collaborative Feature-Combination Recommender Exploiting Explicit and Implicit User Feedback

Zanker, Markus; Jessenitschnig, Markus

doi:10.1109/cec.2009.84

Cited by 20 publications

(13 citation statements)

References 20 publications

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“…The method of [60] works on top of a (process) logs repository and none of the methods presented there [60] can be used in our scenario. For further information on other variants of recommendation mechanisms, we refer to [27,74].…”

Section: Related Workmentioning

confidence: 99%

Recommendation-based editor for business process modeling

Koschmider¹,

Hornung²,

Oberweis³

2011

Data & Knowledge Engineering

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Section: Related Workmentioning

confidence: 99%

Recommendation-based editor for business process modeling

Koschmider¹,

Hornung²,

Oberweis³

2011

Data & Knowledge Engineering

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“…These user opinions are useful to determine the proximity of users' tastes. See the suggestions provided by Amazon, such as "Users who bought the item a, also bought items b and c." (Zanker and Jessenitschnig, 2009) propose a collaborative algorithm for determining similar users and making recommendations, based on a hybrid recommendation approach that utilizes a diverse range of input data, such as clickstream data, sales transactions and explicit user requirements.…”

Section: Place-based Recommender and Social Matching Algorithmsmentioning

confidence: 99%

Current Challenges for Mobile Location-Based Pervasive Content Sharing Application

Francese¹,

Passero²,

Tortora³

2011

Ubiquitous Computing

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“…Finally, the weighted sum of both rating predictions is calculated to combine explicit ratings and implicit feedback, as is commonly done [39].…”

Section: Collaborative Filteringmentioning

confidence: 99%

Hybrid group recommendations for a travel service

Pessemier

Dhondt

Martens

2016

Multimed Tools Appl

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Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations.

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Collaborative Feature-Combination Recommender Exploiting Explicit and Implicit User Feedback

Cited by 20 publications

References 20 publications

Recommendation-based editor for business process modeling

Recommendation-based editor for business process modeling

Current Challenges for Mobile Location-Based Pervasive Content Sharing Application

Hybrid group recommendations for a travel service

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