Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Temporal Proximity induces Attributes Similarity   Users consume their favorite content in temporal proximity of consumption
bundles according to their preferences and tastes. Thus, the underlying
attributes of items implicitly match user preferences, however, current
recommender systems largely ignore this fundamental driver in identifying
matching items. In this work, we introduce a novel temporal proximity filtering
method to enable items-matching. First, we demonstrate that proximity
preferences exist. Second, we present an induced similarity metric in temporal
proximity driven by user tastes and third, we show that this induced similarity
can be used to learn items pairwise similarity in attribute space. The proposed
model does not rely on any knowledge outside users' consumption bundles and
provide a novel way to devise user preferences and tastes driven novel items
recommender.

15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat