Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning from Sets of Items in Recommender Systems   Most of the existing recommender systems use the ratings provided by users on
individual items. An additional source of preference information is to use the
ratings that users provide on sets of items. The advantages of using
preferences on sets are two-fold. First, a rating provided on a set conveys
some preference information about each of the set's items, which allows us to
acquire a user's preferences for more items that the number of ratings that the
user provided. Second, due to privacy concerns, users may not be willing to
reveal their preferences on individual items explicitly but may be willing to
provide a single rating to a set of items, since it provides some level of
information hiding. This paper investigates two questions related to using
set-level ratings in recommender systems. First, how users' item-level ratings
relate to their set-level ratings. Second, how collaborative filtering-based
models for item-level rating prediction can take advantage of such set-level
ratings. We have collected set-level ratings from active users of Movielens on
sets of movies that they have rated in the past. Our analysis of these ratings
shows that though the majority of the users provide the average of the ratings
on a set's constituent items as the rating on the set, there exists a
significant number of users that tend to consistently either under- or
over-rate the sets. We have developed collaborative filtering-based methods to
explicitly model these user behaviors that can be used to recommend items to
users. Experiments on real data and on synthetic data that resembles the under-
or over-rating behavior in the real data, demonstrate that these models can
recover the overall characteristics of the underlying data and predict the
user's ratings on individual items.

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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: acc-phys
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The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: math
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex