Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attainment Ratings for Graph-Query Recommendation   The video game industry is larger than both the film and music industries
combined. Recommender systems for video games have received relatively scant
academic attention, despite the uniqueness of the medium and its data. In this
paper, we introduce a graph-based recommender system that makes use of
interactivity, arguably the most significant feature of video gaming. We show
that the use of implicit data that tracks user-game interactions and levels of
attainment (e.g. Sony Playstation Trophies, Microsoft Xbox Achievements) has
high predictive value when making recommendations. Furthermore, we argue that
the characteristics of the video gaming hobby (low cost, high duration,
socially relevant) make clear the necessity of personalized, individual
recommendations that can incorporate social networking information. We
demonstrate the natural suitability of graph-query based recommendation for
this purpose.

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The categories of this paper are: q-fin
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The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: stat
6
The categories of this paper are: astro-ph
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The categories of this paper are: eess
31
The categories of this paper are: ao-sci
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The categories of this paper are: solv-int