18,603 research outputs found
Community Detection and Growth Potential Prediction from Patent Citation Networks
The scoring of patents is useful for technology management analysis.
Therefore, a necessity of developing citation network clustering and prediction
of future citations for practical patent scoring arises. In this paper, we
propose a community detection method using the Node2vec. And in order to
analyze growth potential we compare three ''time series analysis methods'', the
Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of
our experiments, we could find common technical points from those clusters by
Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model
was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author
Computing large market equilibria using abstractions
Computing market equilibria is an important practical problem for market
design (e.g. fair division, item allocation). However, computing equilibria
requires large amounts of information (e.g. all valuations for all buyers for
all items) and compute power. We consider ameliorating these issues by applying
a method used for solving complex games: constructing a coarsened abstraction
of a given market, solving for the equilibrium in the abstraction, and lifting
the prices and allocations back to the original market. We show how to bound
important quantities such as regret, envy, Nash social welfare, Pareto
optimality, and maximin share when the abstracted prices and allocations are
used in place of the real equilibrium. We then study two abstraction methods of
interest for practitioners: 1) filling in unknown valuations using techniques
from matrix completion, 2) reducing the problem size by aggregating groups of
buyers/items into smaller numbers of representative buyers/items and solving
for equilibrium in this coarsened market. We find that in real data
allocations/prices that are relatively close to equilibria can be computed from
even very coarse abstractions
The Computational Difficulty of Bribery in Qualitative Coalitional Games
Qualitative coalitional games (QCG) are representations of coalitional games in which self interested agents, each with their own individual goals, group together in order to achieve a set of goals which satisfy all the agents within that group. In such a representation, it is the strategy of the agents to find the best coalition to join. Previous work into QCGs has investigated the computational complexity of determining which is the best coalition to join. We plan to expand on this work by investigating the computational complexity of computing agent power in QCGs as well as by showing that insincere strategies, particularly bribery, are possible when the envy-freeness assumption is removed but that it is computationally difficult to identify the best agents to bribe.Bribery, Coalition Formation, Computational Complexity
Strategic Freedom, Constraint, and Symmetry in One-period Markets with Cash and Credit Payment
In order to explain in a systematic way why certain combinations of market, financial, and legal structures may be intrinsic to certain capabilities to exchange real goods, we introduce criteria for abstracting the qualitative functions of markets. The criteria involve the number of strategic freedoms the combined institutions, considered as formalized strategic games, present to traders, the constraints they impose, and the symmetry with which those constraints are applied to the traders. We pay particular attention to what is required to make these "strategic market games" well-defined, and to make various solutions computable by the agents within the bounds on information and control they are assumed to have. As an application of these criteria, we present a complete taxonomy of the minimal one-period exchange economies with symmetric information and inside money. A natural hierarchy of market forms is observed to emerge, in which institutionally simpler markets are often found to be more suitable to fewer and less-diversified traders, while the institutionally richer markets only become functional as the size and diversity of their users gets large.Strategic market games, Clearinghouses, Credit evaluation, Default
Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising
Sponsored search represents a major source of revenue for web search engines.
This popular advertising model brings a unique possibility for advertisers to
target users' immediate intent communicated through a search query, usually by
displaying their ads alongside organic search results for queries deemed
relevant to their products or services. However, due to a large number of
unique queries it is challenging for advertisers to identify all such relevant
queries. For this reason search engines often provide a service of advanced
matching, which automatically finds additional relevant queries for advertisers
to bid on. We present a novel advanced matching approach based on the idea of
semantic embeddings of queries and ads. The embeddings were learned using a
large data set of user search sessions, consisting of search queries, clicked
ads and search links, while utilizing contextual information such as dwell time
and skipped ads. To address the large-scale nature of our problem, both in
terms of data and vocabulary size, we propose a novel distributed algorithm for
training of the embeddings. Finally, we present an approach for overcoming a
cold-start problem associated with new ads and queries. We report results of
editorial evaluation and online tests on actual search traffic. The results
show that our approach significantly outperforms baselines in terms of
relevance, coverage, and incremental revenue. Lastly, we open-source learned
query embeddings to be used by researchers in computational advertising and
related fields.Comment: 10 pages, 4 figures, 39th International ACM SIGIR Conference on
Research and Development in Information Retrieval, SIGIR 2016, Pisa, Ital
Exploratory topic modeling with distributional semantics
As we continue to collect and store textual data in a multitude of domains,
we are regularly confronted with material whose largely unknown thematic
structure we want to uncover. With unsupervised, exploratory analysis, no prior
knowledge about the content is required and highly open-ended tasks can be
supported. In the past few years, probabilistic topic modeling has emerged as a
popular approach to this problem. Nevertheless, the representation of the
latent topics as aggregations of semi-coherent terms limits their
interpretability and level of detail.
This paper presents an alternative approach to topic modeling that maps
topics as a network for exploration, based on distributional semantics using
learned word vectors. From the granular level of terms and their semantic
similarity relations global topic structures emerge as clustered regions and
gradients of concepts. Moreover, the paper discusses the visual interactive
representation of the topic map, which plays an important role in supporting
its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent
Data Analysis (IDA 2015
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