4,080 research outputs found
A methodology for contextual recommendation using artificial neural networks
“A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy”.Recommender systems are an advanced form of software applications, more specifically
decision-support systems, that efficiently assist the users in finding items of their interest.
Recommender systems have been applied to many domains from music to e-commerce,
movies to software services delivery and tourism to news by exploiting available information
to predict and provide recommendations to end user. The suggestions generated by recommender
systems tend to narrow down the list of items which a user may overlook due to the
huge variety of similar items or users’ lack of experience in the particular domain of interest.
While the performance of traditional recommender systems, which rely on relatively simpler
information such as content and users’ filters, is widely accepted, their predictive capability
perfomrs poorly when local context of the user and situated actions have significant role in the
final decision. Therefore, acceptance and incorporation of context of the user as a significant
feature and development of recommender systems utilising the premise becomes an active
area of research requiring further investigation of the underlying algorithms and methodology.
This thesis focuses on categorisation of contextual and non-contextual features within
the domain of context-aware recommender system and their respective evaluation. Further,
application of the Multilayer Perceptron Model (MLP) for generating predictions and ratings
from the contextual and non-contextual features for contextual recommendations is presented
with support from relevant literature and empirical evaluation. An evaluation of specifically
employing artificial neural networks (ANNs) in the proposed methodology is also presented.
The work emphasizes on both algorithms and methodology with three points of consideration:\ud
contextual features and ratings of particular items/movies are exploited in several representations
to improve the accuracy of recommendation process using artificial neural networks
(ANNs), context features are combined with user-features to further improve the accuracy of
a context-aware recommender system and lastly, a combination of the item/movie features
are investigated within the recommendation process. The proposed approach is evaluated on
the LDOS-CoMoDa dataset and the results are compared with state-of-the-art approaches
from relevant published literature
Mining the social semantic Web for making cross-domain recommendations
This is an electronic version of the paper presented at the Fifth BCS-IRSG Symposium on Future Directions in Information Access, held in Granada on 2003Cross-domain recommender systems filter and suggest items in a target domain by exploiting user preferences and/or domain knowledge available in a (likely related) source domain. In our research we are developing a framework for cross-domain recommendation capable of mining heterogeneous sources of information available in the so-called Social Semantic Web, such as semantically annotated data, user generated contents, and contextual signals
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
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Local Popularity and Time in top-N Recommendation
Items popularity is a strong signal in recommendation algorithms. It strongly
affects collaborative filtering approaches and it has been proven to be a very
good baseline in terms of results accuracy. Even though we miss an actual
personalization, global popularity can be effectively used to recommend items
to users. In this paper we introduce the idea of a time-aware personalized
popularity in recommender systems by considering both items popularity among
neighbors and how it changes over time. An experimental evaluation shows a
highly competitive behavior of the proposed approach, compared to state of the
art model-based collaborative approaches, in terms of results accuracy.Comment: ECIR short paper, 7 page
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