System and Method for Recommending Semantically Relevant Content

Abstract

A property vector derived from extractable measurable properties of a data file is mapped to semantic properties for that data file. The property vector is an output from a trained artificial neural network that, following pairwise training of the ANN using pairs of files that map pairwise similarity/dissimilarity in property space towards corresponding pairwise semantic similarity/dissimilarity in semantic space, both preserves and is representative of semantic properties of the data file. The system and method assesses, based on comparisons between generated property vectors, ranks and then recommends and/or filters semantically close or semantically disparate candidate files in a database from a query from a user that includes the data file. Applications of the categorization and recommendation system and method apply to media or search tools and social media platforms, including media in the form of music, video, images data and/or text files.

Description

BACKGROUND TO THE INVENTION[0001]This invention relates, in general, to artificial intelligence and neural networks that generate a property-based file vector derived from extracted properties from a data file and where the property-based file vector both preserves and is representative of semantic properties of the data file. More particularly, the present invention relates to a system and method for assessing, ranking and then recommending and / or filtering semantically close or semantically disparate candidate files in a database in response to a query from a user, especially but not exclusively to a query in an internet-based platform such as a social media platform or search engine. More especially, applications of the categorization and recommendation system and method apply to media in the form of music, video and / or images data files, although the application widely applicable and finds application in evaluation of speech and text data files, including personal reports.SUMMAR...

AI Technical Summary

Benefits of technology

[0056]Advantageously, the present invention provides an innovative methodology for data categorization and, more particularly, a system and method that permits rapid assimilation of user-perceivable qualities between original data and possible relevant search data, e.g. detection of audio or sections of an audio file that are likely to warrant a listener's interest. The approach applies equally to image, video, text and speech data files or a combination of two or more of these file types.

[0057]A preferred embodiment, amongst other things, provides a track finder or track recommendation tool that is able to consistently characterize a file by distilling out identifiable properties in a section thereof, and then to identify other files that commonly share those characteristics and/or subjective qualities.

[0058]Given the number of accessible files, including variations that can subtly or significantly change the original file, within data libraries (whether personal ones containing hundreds or a few thousand files or commercial libraries having millions of files for commercial streaming, download or reference) the present invention provides a useful and effective recommendation tool that hones search results for files...

Problems solved by technology

One of the most challenging long-term objectives for artificial intelligence “AI”, typically based on an artificial neural network architecture, is to replicate human intellectual behaviour.

This is a complex proposition not least because human opinion is based on subjective responses to stimuli and existing approaches in AI do not correlate well with emotional perspective responses.

This may, at first inspection, not appear problematic, but on an intellectual and real footing a fundamental problem remains because “there is no such thing as music, except as created, perceived, and experienced in the human mind.

Hence, existing AI modelling that, from its outset, is based on a degree of absoluteness (based on the interpretation of measured parameters) is fatally flawed with the consequence that it will generate, in the exemplary context of a musical search tool, inconsistent and/or spurious results.

The same problems exist with the identification and categorization of other forms of expression, such as paintings or photographs or indeed interpretations of imagery, such as medical CT scans, or other purely descriptive expressions (such as a description of a smell, a medical report or an outline of a plot in a work of fiction) to locate and assess, relative to a defined start point (e.g. a particular description of a fragrance or the tonality, rhythm and timbre of a musical composition), the relevance of searchable electronic images and/or data that are either entirely unrelated or otherwise are potentially relevant to one another from the perspective of having an acceptably close set of subjective attributes, qualities or characteristics.

In fact, existing AI systems cannot resolve semantically-relevant attributes and therefore can both overlook semantic similarities whilst accepting or suggesting that perceptually-distinct dissimilarities are closely related.

Whilst music sales are commercial and content perceptual and aesthetic in nature, there is no existing, straightforward and reliable mechanisms to locate tracks that share common musical characteristics honed to an individual's specific tastes.

These sub-species may share some overarching similarities in user-discernible compositional architectures that define the genus, but frequently there are also significant dissimilarities that are sufficiently audibly or musically pronounced.

With typically online music libraries each containing millions of songs—the iTunes and Tidal® music libraries allegedly each contain around fifty million tracks—the problem exists about how these databases can be effectively searched to identify user-perceived common musical themes, traits or features between myriad tracks potentially spanning entirely different genres.

Collaborative filtering can reflect the personal preferences of a listener/user of the library, but it is limited by the amount of user data available and so is not in itself a...

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