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The API includes access into StyleFeeder's personalized search engine, bookmarking tools, item recommendations, watchlist, "StyleFeed", "StyleTwins" and other features.
That's what Hollrr's destination site is for — it features a real-time stream of recent item recommendations, which these users can then take and share back to Facebook and Twitter (and look smart in the process).
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Especially for the item recommendation problem, we should recommend items for a user from all the items.
As future work, we plan to analyze the proposed set of feature extraction techniques with other recommender algorithms, including those specific for the item recommendation task and optimization of personalized rankings, such as the BPR (Bayesian Personalized Ranking) [31], CliMF (Collaborative Less-is-More Filtering) [32], and TFMAP (Tensor Factorization for MAP Maximization) [33].
Only few trust-aware methods have attempted to recommend users an ordered list of interesting items, i.e., item recommendation.
Both approaches have some advantages and disadvantages for item recommendation.
Item recommendation helps people to discover their potentially interested items among large numbers of items.
Based on item recommendation problem, we further propose a variant method user-based LWMF, which is more reasonable for item recommendation and get better performance.
Therefore, we consider doing top-n item recommendation based on implicit feedback datasets.
In this section, we present the results related to the item recommendation scenario.
Item recommendation evaluates the capacity of the system to generate personalized rankings of suggestions.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com