10,419 research outputs found
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
A large multilingual and multi-domain dataset for recommender systems
This paper presents a multi-domain interests dataset to train and test Recommender Systems, and the methodology to create the dataset
from Twitter messages in English and Italian. The English dataset includes an average of 90 preferences per user on music, books,
movies, celebrities, sport, politics and much more, for about half million users. Preferences are either extracted from messages of
users who use Spotify, Goodreads and other similar content sharing platforms, or induced from their ”topical” friends, i.e., followees
representing an interest rather than a social relation between peers. In addition, preferred items are matched with Wikipedia articles
describing them. This unique feature of our dataset provides a mean to derive a semantic categorization of the preferred items, exploiting
available semantic resources linked to Wikipedia such as the Wikipedia Category Graph, DBpedia, BabelNet and others
Web Data Extraction, Applications and Techniques: A Survey
Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc domains. Other approaches, instead, heavily
reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
literature in the field of Web Data Extraction. We provided a simple
classification framework in which existing Web Data Extraction applications are
grouped into two main classes, namely applications at the Enterprise level and
at the Social Web level. At the Enterprise level, Web Data Extraction
techniques emerge as a key tool to perform data analysis in Business and
Competitive Intelligence systems as well as for business process
re-engineering. At the Social Web level, Web Data Extraction techniques allow
to gather a large amount of structured data continuously generated and
disseminated by Web 2.0, Social Media and Online Social Network users and this
offers unprecedented opportunities to analyze human behavior at a very large
scale. We discuss also the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.Comment: Knowledge-based System
Movies Tags Extraction Using Deep Learning
Retrieving information from movies is becoming increasingly
demanding due to the enormous amount of multimedia
data generated each day. Not only it helps in efficient
search, archiving and classification of movies, but is also instrumental
in content censorship and recommendation systems.
Extracting key information from a movie and summarizing
it in a few tags which best describe the movie presents
a dedicated challenge and requires an intelligent approach
to automatically analyze the movie. In this paper, we formulate
movies tags extraction problem as a machine learning
classification problem and train a Convolution Neural Network
(CNN) on a carefully constructed tag vocabulary. Our
proposed technique first extracts key frames from a movie
and applies the trained classifier on the key frames. The
predictions from the classifier are assigned scores and are
filtered based on their relative strengths to generate a compact
set of most relevant key tags. We performed a rigorous
subjective evaluation of our proposed technique for a
wide variety of movies with different experiments. The evaluation
results presented in this paper demonstrate that our
proposed approach can efficiently extract the key tags of a
movie with a good accuracy
Semantic Technologies for Manuscript Descriptions — Concepts and Visions
The contribution at hand relates recent developments in the area of the World Wide
Web to codicological research. In the last number of years, an informational extension
of the internet has been discussed and extensively researched: the Semantic Web. It
has already been applied in many areas, including digital information processing of
cultural heritage data. The Semantic Web facilitates the organisation and linking of
data across websites, according to a given semantic structure. Software can then process
this structural and semantic information to extract further knowledge. In the area
of codicological research, many institutions are making efforts to improve the online
availability of handwritten codices. If these resources could also employ Semantic
Web techniques, considerable research potential could be unleashed. However, data
acquisition from less structured data sources will be problematic. In particular, data
stemming from unstructured sources needs to be made accessible to SemanticWeb tools
through information extraction techniques. In the area of museum research, the CIDOC
Conceptual Reference Model (CRM) has been widely examined and is being adopted
successfully. The CRM translates well to Semantic Web research, and its concentration
on contextualization of objects could support approaches in codicological research.
Further concepts for the creation and management of bibliographic coherences and
structured vocabularies related to the CRM will be considered in this chapter. Finally, a
user scenario showing all processing steps in their context will be elaborated on
Reply With: Proactive Recommendation of Email Attachments
Email responses often contain items-such as a file or a hyperlink to an
external document-that are attached to or included inline in the body of the
message. Analysis of an enterprise email corpus reveals that 35% of the time
when users include these items as part of their response, the attachable item
is already present in their inbox or sent folder. A modern email client can
proactively retrieve relevant attachable items from the user's past emails
based on the context of the current conversation, and recommend them for
inclusion, to reduce the time and effort involved in composing the response. In
this paper, we propose a weakly supervised learning framework for recommending
attachable items to the user. As email search systems are commonly available,
we constrain the recommendation task to formulating effective search queries
from the context of the conversations. The query is submitted to an existing IR
system to retrieve relevant items for attachment. We also present a novel
strategy for generating labels from an email corpus---without the need for
manual annotations---that can be used to train and evaluate the query
formulation model. In addition, we describe a deep convolutional neural network
that demonstrates satisfactory performance on this query formulation task when
evaluated on the publicly available Avocado dataset and a proprietary dataset
of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on
Information and Knowledge Management. 201
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