Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic Modeling of User Preferences for Stable Recommendations   In domains where users tend to develop long-term preferences that do not
change too frequently, the stability of recommendations is an important factor
of the perceived quality of a recommender system. In such cases, unstable
recommendations may lead to poor personalization experience and distrust,
driving users away from a recommendation service. We propose an incremental
learning scheme that mitigates such problems through the dynamic modeling
approach. It incorporates a generalized matrix form of a partial differential
equation integrator that yields a dynamic low-rank approximation of
time-dependent matrices representing user preferences. The scheme allows
extending the famous PureSVD approach to time-aware settings and significantly
improves its stability without sacrificing the accuracy in standard top-$n$
recommendations tasks.

27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
9
The categories of this paper are: nlin
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th