6,935 research outputs found
Joint Topic-Semantic-aware Social Recommendation for Online Voting
Online voting is an emerging feature in social networks, in which users can
express their attitudes toward various issues and show their unique interest.
Online voting imposes new challenges on recommendation, because the propagation
of votings heavily depends on the structure of social networks as well as the
content of votings. In this paper, we investigate how to utilize these two
factors in a comprehensive manner when doing voting recommendation. First, due
to the fact that existing text mining methods such as topic model and semantic
model cannot well process the content of votings that is typically short and
ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to
learn word and document representation by jointly considering their topics and
semantics. Then we propose our Joint Topic-Semantic-aware social Matrix
Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates
similarity among users and votings by combining their TEWE representation and
structural information of social networks, and preserves this
topic-semantic-social similarity during matrix factorization. To evaluate the
performance of TEWE representation and JTS-MF model, we conduct extensive
experiments on real online voting dataset. The results prove the efficacy of
our approach against several state-of-the-art baselines.Comment: The 26th ACM International Conference on Information and Knowledge
Management (CIKM 2017
NeMig -- A Bilingual News Collection and Knowledge Graph about Migration
News recommendation plays a critical role in shaping the public's worldviews
through the way in which it filters and disseminates information about
different topics. Given the crucial impact that media plays in opinion
formation, especially for sensitive topics, understanding the effects of
personalized recommendation beyond accuracy has become essential in today's
digital society. In this work, we present NeMig, a bilingual news collection on
the topic of migration, and corresponding rich user data. In comparison to
existing news recommendation datasets, which comprise a large variety of
monolingual news, NeMig covers articles on a single controversial topic,
published in both Germany and the US. We annotate the sentiment polarization of
the articles and the political leanings of the media outlets, in addition to
extracting subtopics and named entities disambiguated through Wikidata. These
features can be used to analyze the effects of algorithmic news curation beyond
accuracy-based performance, such as recommender biases and the creation of
filter bubbles. We construct domain-specific knowledge graphs from the news
text and metadata, thus encoding knowledge-level connections between articles.
Importantly, while existing datasets include only click behavior, we collect
user socio-demographic and political information in addition to explicit click
feedback. We demonstrate the utility of NeMig through experiments on the tasks
of news recommenders benchmarking, analysis of biases in recommenders, and news
trends analysis. NeMig aims to provide a useful resource for the news
recommendation community and to foster interdisciplinary research into the
multidimensional effects of algorithmic news curation.Comment: Accepted at the 11th International Workshop on News Recommendation
and Analytics (INRA 2023) in conjunction with ACM RecSys 202
Content Modelling for unbiased Information Analysis
Content is the form through which the information is conveyed as per the requirement of user. A volume of content is huge and expected to grow exponentially hence classification of useful data and not useful data is a very tedious task. Interface between content and user is Search engine. Therefore, the contents are designed considering search engine\u27s perspective. Content designed by the organization, utilizes user’s data for promoting their products and services. This is done mostly using inorganic ways utilized to influence the quality measures of a content, this may mislead the information. There is no correct mechanism available to analyse and disseminate the data. The gap between Actual results displayed to the user and results expected by the user can be minimized by introducing the quality check for the parameter to assess the quality of content. This may help to ensure the quality of content and popularity will not be allowed to precede quality of content. Social networking sites will help in doing the user modelling so that the qualitative dissemination of content can be validated
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
Where previous reviews on content-based image retrieval emphasize on what can
be seen in an image to bridge the semantic gap, this survey considers what
people tag about an image. A comprehensive treatise of three closely linked
problems, i.e., image tag assignment, refinement, and tag-based image retrieval
is presented. While existing works vary in terms of their targeted tasks and
methodology, they rely on the key functionality of tag relevance, i.e.
estimating the relevance of a specific tag with respect to the visual content
of a given image and its social context. By analyzing what information a
specific method exploits to construct its tag relevance function and how such
information is exploited, this paper introduces a taxonomy to structure the
growing literature, understand the ingredients of the main works, clarify their
connections and difference, and recognize their merits and limitations. For a
head-to-head comparison between the state-of-the-art, a new experimental
protocol is presented, with training sets containing 10k, 100k and 1m images
and an evaluation on three test sets, contributed by various research groups.
Eleven representative works are implemented and evaluated. Putting all this
together, the survey aims to provide an overview of the past and foster
progress for the near future.Comment: to appear in ACM Computing Survey
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