5,831 research outputs found
On the additivity of preference aggregation methods
The paper reviews some axioms of additivity concerning ranking methods used
for generalized tournaments with possible missing values and multiple
comparisons. It is shown that one of the most natural properties, called
consistency, has strong links to independence of irrelevant comparisons, an
axiom judged unfavourable when players have different opponents. Therefore some
directions of weakening consistency are suggested, and several ranking methods,
the score, generalized row sum and least squares as well as fair bets and its
two variants (one of them entirely new) are analysed whether they satisfy the
properties discussed. It turns out that least squares and generalized row sum
with an appropriate parameter choice preserve the relative ranking of two
objects if the ranking problems added have the same comparison structure.Comment: 24 pages, 9 figure
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation
Event extraction is of practical utility in natural language processing. In
the real world, it is a common phenomenon that multiple events existing in the
same sentence, where extracting them are more difficult than extracting a
single event. Previous works on modeling the associations between events by
sequential modeling methods suffer a lot from the low efficiency in capturing
very long-range dependencies. In this paper, we propose a novel Jointly
Multiple Events Extraction (JMEE) framework to jointly extract multiple event
triggers and arguments by introducing syntactic shortcut arcs to enhance
information flow and attention-based graph convolution networks to model graph
information. The experiment results demonstrate that our proposed framework
achieves competitive results compared with state-of-the-art methods.Comment: accepted by EMNLP 201
Vote and aggregation in combinatorial domains with structured preferences
In many real-world collective decision problems, the set of alternatives is a Cartesian product of finite value domains for each of a given set of variables. The prohibitive size of such combinatorial domains makes it practically impossible to represent preference relations explicitly. Now, the AI community has been developing languages for representing preferences on such domains in a succinct way, exploiting structural properties such as conditional preferential independence. In this paper we reconsider voting and aggregation rules in the case where voters' preferences have a common preferential independence structure, and address the issue of decomposing a voting rule or an aggregation function following a linear order over variables
A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists
User-generated item lists are a popular feature of many different platforms.
Examples include lists of books on Goodreads, playlists on Spotify and YouTube,
collections of images on Pinterest, and lists of answers on question-answer
sites like Zhihu. Recommending item lists is critical for increasing user
engagement and connecting users to new items, but many approaches are designed
for the item-based recommendation, without careful consideration of the complex
relationships between items and lists. Hence, in this paper, we propose a novel
user-generated list recommendation model called AttList. Two unique features of
AttList are careful modeling of (i) hierarchical user preference, which
aggregates items to characterize the list that they belong to, and then
aggregates these lists to estimate the user preference, naturally fitting into
the hierarchical structure of item lists; and (ii) item and list consistency,
through a novel self-attentive aggregation layer designed for capturing the
consistency of neighboring items and lists to better model user preference.
Through experiments over three real-world datasets reflecting different kinds
of user-generated item lists, we find that AttList results in significant
improvements in NDCG, Precision@k, and Recall@k versus a suite of
state-of-the-art baselines. Furthermore, all code and data are available at
https://github.com/heyunh2015/AttList.Comment: Accepted by CIKM 201
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