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  • Divinatory Recommender Systems: between Similarity and Serendipity

    Recommender systems are the algorithms that determine what content we read, which products we buy, which movies we watch. However, most of them are based on similarity and lock us into “filter bubble” where we see only what we expect. In this article we discuss how to bring in more serendipity into the algorithms.

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  • Clean Facebook Newsfeed, Explode Filter Bubble

    Facebook newsfeed is a huge distraction for users, producing unnecessary time waste and a false sense of social engagement. Here we present an app that eradicates Facebook newsfeed and replaces it with some interesting info, advice, and links to the more interesting Facebook features (such as Graph search).

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  • From Cognitive Interfaces to Transcendental Protocols

    Anything can be an interface, as long as it is in between and as everything is in between, everything is an interface. Interfaces create affordances or possibilities for action. They affect our perception and cognition. How can the interfaces with such transformative potential be designed? Moreover, if interface create a set of actions, what if we go beyond the notion of interfaces and think in terms of the protocols? Not the kind of protocols that say to do this and not to do that. The kind of protocols that transcend reality, that lie in the realm of practices, such as meditation, artistic practices, version-control systems, BitCoin, chaotic itinerancy, and other polysingular approaches.

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  • Text Network Analysis for SEO

    How text network analysis can be used for website SEO. We use an example of a travel guide website to find out what people are searching for in relation to travel, what they actually find, and how well the travel guide website studied responds to the needs of the users. We also explore the idea of content polysingularity ensuring that information offered within the website does not only fulfil the readers’ needs, but also helps them learn more and discover new perspectives about the topics they’re interested in.

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  • Group Profiling Using Text Network Analysis

    Any group of people working together has a field of interests, which can be represented as a network of interrelated concepts. Mark might be interested in studying trends and fashion, while Joanne might be into fashion and arts. Identifying those interests and their relation to one another can be very useful for improving the collaboration, indicating the vantage points as well as the structural gaps within the group.

    Text network analysis can be used for such quick group profiling. The members of the group can write a short text (or be interviewed) outlining their interests. Alternatively, publicly available texts can be used to gather the data. Next, graph visualization of this aggregated text can help identify the most prominent topics and their relations within that text corpus.

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