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MiSPOT: dynamic product placement for digital TV through MPEG4 processing and semantic reasoning

MiSPOT: dynamic product placement for digital TV through MPEG4 processing and semantic reasoning,10.1007/s10115-009-0200-8,Knowledge and Information S

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MiSPOT: dynamic product placement for digital TV through MPEG4 processing and semantic reasoning   (Citations: 8)
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In an increasingly competitive market, stakeholders of the television industry strive to exploit all the possibilities to get revenues from advertising, but their practices are usually at odds with the comfort of the TV viewers. This paper presents the proof of concept of MiSPOT, a system that brings a non-invasive and fully personalized form of advertising to Interactive Digital TV, targeting both domestic and mobile receivers. MiSPOT employs semantic reasoning techniques to select advertisements suited to the preferences, interests and needs of each individual viewer, and then relies on multimedia composition abilities to blend the advertising material with the TV program he/she is viewing at any time. The advertisements can be set to launch interactive commercials, thus enabling means for the provision of t-commerce services. Evaluation experiments are described to show the technical viability of the proposal, and also to gauge the opinions of end users. Questions about the potential impact and exploitation of this new form of advertising are addressed too.
Journal: Knowledge and Information Systems - KAIS , vol. 22, no. 1, pp. 101-128, 2010
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    • ...We analyzed the users' satisfaction with the recommendations produced by three different engines: (i) MiSPOT, a healthunaware system based on UBCF [7], (ii) the first version of HARE, which could revise the outputs of MiSPOT according to health-related data (details in [1]), and (iii) the new, PBCFenabled version of HARE...
    • ...None of the systems started from scratch, as we could rely on a knowledge base –essentially, an extensive collection of user ratings– built from previous experiments with MiSPOT (see [7])...

    Martin Lopez-Nores, et al. Property-based collaborative filtering for health-aware recommender sy...

    • ...Semantic Web have been adopted in recommender systems to successfully alleviate recurring weaknesses [10]-[12]...

    Manuela I. Martín-Vicente, et al. A semantic approach to avoiding fake neighborhoods in collaborative re...

    • ... in web navigation or TV watching records [5, 46], learning histories [9, 39], consumption profiles [26, 28], electronic health records [6, 25, 29] and so on. Various authors have even combined several such sources to exploit synergies between different areas, for example, to enhance TV programs with educational contents related by topic [43], to situate advertising material within TV shows and thereupon launch interactive commercials [30], ...
    • ...rating our proposal in MiSPOT, a recommender system devised to embed selected advertisements in TV programs [30]...
    • ...This is indeed an evolution of the approach to receiver-side semantic reasoning we presented in [30] to deliver recommendations of TV programs and/or commercial products that the viewers may be interested in, though not involving any kind of health-related information...
    • ...It is worth noting that, in order to introduce health-related concerns in the recommendation logic, we have enhanced the ontologies formerly used in [30] to match TV programs with commercial products and services, which involved metadata from the TV-Anytime, MPEG-7 and eCl@ss standards...
    • ...At this point, we use a classical (cumulative) semantic similarity metrics that looks at relationships between the attributes of the TV program being broadcast and those of the items available to recommendation (indeed, it is the same metrics of [30]) —thus, we hitherto treat the health-related features of the items just like any other attributes, because TV audiences may involve people with very diverse health conditions; the ...
    • ...Finally, as we announced in Section 1, we have developed mechanisms for the set-top boxes to interact safely with EHR repositories, and also enhanced the filtering algorithms of [30] to adjust the recommendations to the viewer’s health-related information...
    • ...• Firstly, we apply the semantic similarity metrics presented in [30] to rate the items’ relevance between −1 (the lowest value) and +1 (the highest value) according to the two main strategies in literature (remember Section 2): content-based filtering and collaborative filtering...

    Martín López-Nores, et al. Exploring synergies between digital tv recommender systems and electro...

    • ...Semantics has been lately adopted in recommender systems to successfully alleviate the above-mentioned recurring problems (sparsity and scalability) [10], [11], [12]...
    • ...Then Neighbor1 and Neighbor2 form User’s neighborhood and White tea is recommended to User pursuant to [11]...

    Manuela I. Martín-Vicente, et al. Avoiding Fake Neighborhoods in e-Commerce Collaborative Recommender Sy...

    • ...We have realized the aforementioned vision by enhancing the recommender system of MiSPOT [7], originally designed to identify the best items to advertise within TV programs...
    • ...There are also several systems that aim to personalize TV advertising, by means of simple heuristics [9], syntactic matching techniques [10,11] or semantic reasoning [7]...
    • ...Items and programs are linked to a number between 0 and 1 that represents the level of interest of the user in them; this number may be explicitly provided by the user, or inferred from ratings given to other related concepts in the domain ontology (see [7] for the details)...
    • ...In order to decide whether a given item might be appealing to a user, the system computes a matching level between his preferences and the semantic annotations of the item in the domain ontology, measuring similarity in terms of common ancestors in the items taxonomy, common attributes and sibling attributes (again, details can be found in [7])...
    • ...To this aim, we have extended the architectural solutions presented in [7] for the recommendation of items, to embrace also the generation of i-spots...
    • ...We had shown in [7] that the item recommendations provided by the client-side scheme are less accurate than the ones provided by the server-side scheme, because the latter can involve much more knowledge about users and items...
    • ...As part of our ongoing work, we plan to continue the experiments initiated in [7] to assess the interest and viability of the MiSPOT innovations in practice...

    Yolanda Blanco-Fernández, et al. Automatic Generation of Mashups for Personalized Commerce in Digital T...

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