- User-configurable and interpretable: Enable tuning or re-ranking of results, ideally based on the ability to reweight model internals in a “fuzzy” way. As an example, see the last comment in my history about using convolutional filters on song spectrograms to distill hundreds of latent auditory features (e.g. Chinese, vocal triads, deep-housey). Imagine being able to directly recombine these features, generating a new set of recommendations dynamically. Almost all recommendation engines fail in this regard—the model feeds the user exactly what the model (designer) wants, no more and no less.
- Encourage serendipity: i.e. purposefully select and recommend items that the model “thinks” is outside the user’s wheelhouse (wheelhouse = whatever naturally emerging cluster(s) in the data that the user hangs out in, so pluck out examples from both nearby and distant clusters). This not only helps users break out of local minima, but is healthy for the data feedback loop.
- User-configurable and interpretable: Enable tuning or re-ranking of results, ideally based on the ability to reweight model internals in a “fuzzy” way. As an example, see the last comment in my history about using convolutional filters on song spectrograms to distill hundreds of latent auditory features (e.g. Chinese, vocal triads, deep-housey). Imagine being able to directly recombine these features, generating a new set of recommendations dynamically. Almost all recommendation engines fail in this regard—the model feeds the user exactly what the model (designer) wants, no more and no less.
- Encourage serendipity: i.e. purposefully select and recommend items that the model “thinks” is outside the user’s wheelhouse (wheelhouse = whatever naturally emerging cluster(s) in the data that the user hangs out in, so pluck out examples from both nearby and distant clusters). This not only helps users break out of local minima, but is healthy for the data feedback loop.