Automatic metadata expansion and indirect collaborative filtering for TV program recommendation system

Tsunoda, Tomohiro; Hoshino, Masaaki

doi:10.1007/s11042-006-0077-4

Cited by 29 publications

(15 citation statements)

References 8 publications

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“…More recent works in this area are [61,22,53,11]. Authors in [61] present the architecture of a personalized TV system on the basis of TV-Anytime 2 metadata.…”

Section: Previous Research In Tv Program Recommendation Systemsmentioning

confidence: 99%

“…Similar to our content-based recommender, the vector space model is also used in [53]. In this interesting work, the vector space model is also used in a novel ICF (Indirect CF) algorithm, where the user preferences vector is replaced by the predicted preferences vector, which is obtained by making use of a user-based CF algorithm.…”

Section: Previous Research In Tv Program Recommendation Systemsmentioning

confidence: 99%

“…Moreover, other similar programs, in the sense that they have received the same pattern of ratings, are shown next to the recommended TV show. This is of great value to enhance overall user trust in the system [19,53,31].…”

Section: A Collaborative Filtering Approachmentioning

confidence: 99%

“…The vector space model has been used to retrieve documents [25], and nowadays, it is often used in TV program recommendation systems [53].…”

Section: Content-based Filteringmentioning

confidence: 99%

See 3 more Smart Citations

A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition

Martínez¹,

Costa-Montenegro²,

Burguillo³

et al. 2010

Information Sciences

316

118

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“…More recent works in this area are [61,22,53,11]. Authors in [61] present the architecture of a personalized TV system on the basis of TV-Anytime 2 metadata.…”

Section: Previous Research In Tv Program Recommendation Systemsmentioning

confidence: 99%

Section: Previous Research In Tv Program Recommendation Systemsmentioning

confidence: 99%

Section: A Collaborative Filtering Approachmentioning

confidence: 99%

“…The vector space model has been used to retrieve documents [25], and nowadays, it is often used in TV program recommendation systems [53].…”

Section: Content-based Filteringmentioning

confidence: 99%

See 2 more Smart Citations

A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition

Martínez¹,

Costa-Montenegro²,

Burguillo³

et al. 2010

Information Sciences

316

118

View full text Add to dashboard Cite

“…On the one hand, the preferences of the system's users may be subject to change, as they are affected by seasonal trends or discover new content. Evolution of preferences can be modeled as a decay, so that, in the longer term, parts of users' profiles can expire [28]. Koren [19] also examined this problem, distinguishing between the transient and long term patterns of user rating behaviors, so that only the relevant components of rating data can be taken into account when predicting preferences.…”

Section: Related Workmentioning

confidence: 99%

Using control theory for stable and efficient recommender systems

Jambor

Wang

Lathia

2012

Proceedings of the 21st International Conference on World Wide Web

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The aim of a web-based recommender system is to provide highly accurate and up-to-date recommendations to its users; in practice, it will hope to retain its users over time. However, this raises unique challenges. To achieve complex goals such as keeping the recommender model upto-date over time, we need to consider a number of external requirements. Generally, these requirements arise from the physical nature of the system, for instance the available computational resources. Ideally, we would like to design a system that does not deviate from the required outcome. Modeling such a system over time requires to describe the internal dynamics as a combination of the underlying recommender model and the its users' behavior. We propose to solve this problem by applying the principles of modern control theory-a powerful set of tools to deal with dynamical systems-to construct and maintain a stable and robust recommender system for dynamically evolving environments. In particular, we introduce a design principle by focusing on the dynamic relationship between the recommender system's performance and the number of new training samples the system requires. This enables us to automate the control other external factors such as the system's update frequency. We show that, by using a Proportional-Integral-Derivative controller, a recommender system is able to automatically and accurately estimate the required input to keep the output close to a pre-defined requirements. Our experiments on a standard rating dataset show that, by using a feedback loop between system performance and training, the tradeoff between the effectiveness and efficiency of the system can be well maintained. We close by discussing the widespread applicability of our approach to a variety of scenarios that recommender systems face.

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