Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Personalized Item-to-Item Recommendation Metric via Implicit
  Feedback   This paper studies the item-to-item recommendation problem in recommender
systems from a new perspective of metric learning via implicit feedback. We
develop and investigate a personalizable deep metric model that captures both
the internal contents of items and how they were interacted with by users.
There are two key challenges in learning such model. First, there is no
explicit similarity annotation, which deviates from the assumption of most
metric learning methods. Second, these approaches ignore the fact that items
are often represented by multiple sources of meta data and different users use
different combinations of these sources to form their own notion of similarity.
  To address these challenges, we develop a new metric representation embedded
as kernel parameters of a probabilistic model. This helps express the
correlation between items that a user has interacted with, which can be used to
predict user interaction with new items. Our approach hinges on the intuition
that similar items induce similar interactions from the same user, thus fitting
a metric-parameterized model to predict an implicit feedback signal could
indirectly guide it towards finding the most suitable metric for each user. To
this end, we also analyze how and when the proposed method is effective from a
theoretical lens. Its empirical effectiveness is also demonstrated on several
real-world datasets.

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The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
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The categories of this paper are: math
35
The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
16
The categories of this paper are: stat
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph