6,864 research outputs found
Explanation plug-in for stream-based collaborative filtering
Collaborative filtering is a widely used recommendation technique, which often relies on rating information shared by users, i.e., crowdsourced data. These filters rely on predictive algorithms, such as, memory or model based predictors, to build direct or latent user and item profiles from crowdsourced data. To predict unknown ratings, memory-based approaches rely on the similarity between users or items, whereas model-based mechanisms explore user and item latent profiles. However, many of these filters are opaque by design, leaving users with unexplained recommendations. To overcome this drawback, this paper introduces Explug, a local model-agnostic plug-in that works alongside stream-based collaborative filters to reorder and explain recommendations. The explanations are based on incremental user Trust & Reputation profiling and co-rater relationships. Experiments performed with crowdsourced data from TripAdvisor show that Explug explains and improves the quality of stream-based collaborative filter recommendations.Xunta de Galicia | Ref. ED481B-2021-118Fundação para a Ciência e a Tecnologia | Ref. UIDB/50014/202
The state-of-the-art in personalized recommender systems for social networking
With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0
Analysing and using subjective criteria to improve dental care recommendation systems
Online reviews and rating sites are shaping industries as the users rely on recommendations given by former consumers and sharing opinions on the web. Dentistry has also been impacted by dental patients' reviews. This paper classifies trust-related information for dental care recommendations onto 4 categories: context, relationship, reputation and subjective criteria. It discusses each category and describes how they help focussing on trust when matching patients and dentists in brief. The paper then focuses on subjective criteria and presents the results of a survey aimed at showing trustrelated information emerged from subjective characteristics. Traits of personalities are used as subjective characteristics of patients and that of dentists are derived from the online patients' reviews. 580 Australian patients were surveyed to determine what factors affect their decision to find the trusted dentist. Subjective characteristics of dentists such as dentists' qualities and experienced dentists are considered the most important factors after location and cost. The most preferred dentists' qualities by almost all types of personalities are experienced, professional and quality of service. When the patients are further classified based on levels of fear, their preferences for dentists' qualities changed. Subjective qualities of both patients and dentists are important factors to improve the matching capability for the dental care recommendation systems
Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.
This report gives an overview of the most relevant organisational and\ud
behavioural aspects regarding user profiling. It discusses not only the\ud
most important aims of user profiling from both an organisation’s as\ud
well as a user’s perspective, it will also discuss organisational motives\ud
and barriers for user profiling and the most important conditions for\ud
the success of user profiling. Finally recommendations are made and\ud
suggestions for further research are given
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
Trust and Reputation Modelling for Tourism Recommendations Supported by Crowdsourcing
Tourism crowdsourcing platforms have a profound influence
on the tourist behaviour particularly in terms of travel planning. Not
only they hold the opinions shared by other tourists concerning tourism
resources, but, with the help of recommendation engines, are the pillar
of personalised resource recommendation. However, since prospective
tourists are unaware of the trustworthiness or reputation of crowd publishers,
they are in fact taking a leap of faith when then rely on the
crowd wisdom. In this paper, we argue that modelling publisher Trust &
Reputation improves the quality of the tourism recommendations supported
by crowdsourced information. Therefore, we present a tourism
recommendation system which integrates: (i) user profiling using the
multi-criteria ratings; (ii) k-Nearest Neighbours (k-NN) prediction of the
user ratings; (iii) Trust & Reputation modelling; and (iv) incremental
model update, i.e., providing near real-time recommendations. In terms
of contributions, this paper provides two different Trust & Reputation
approaches: (i) general reputation employing the pairwise trust values
using all users; and (ii) neighbour-based reputation employing the pairwise
trust values of the common neighbours. The proposed method was
experimented using crowdsourced datasets from Expedia and TripAdvisor
platforms.info:eu-repo/semantics/publishedVersio
adPerf: Characterizing the Performance of Third-party Ads
Monetizing websites and web apps through online advertising is widespread in
the web ecosystem. The online advertising ecosystem nowadays forces publishers
to integrate ads from these third-party domains. On the one hand, this raises
several privacy and security concerns that are actively studied in recent
years. On the other hand, given the ability of today's browsers to load dynamic
web pages with complex animations and Javascript, online advertising has also
transformed and can have a significant impact on webpage performance. The
performance cost of online ads is critical since it eventually impacts user
satisfaction as well as their Internet bill and device energy consumption.
In this paper, we apply an in-depth and first-of-a-kind performance
evaluation of web ads. Unlike prior efforts that rely primarily on adblockers,
we perform a fine-grained analysis on the web browser's page loading process to
demystify the performance cost of web ads. We aim to characterize the cost by
every component of an ad, so the publisher, ad syndicate, and advertiser can
improve the ad's performance with detailed guidance. For this purpose, we
develop an infrastructure, adPerf, for the Chrome browser that classifies page
loading workloads into ad-related and main-content at the granularity of
browser activities (such as Javascript and Layout). Our evaluations show that
online advertising entails more than 15% of browser page loading workload and
approximately 88% of that is spent on JavaScript. We also track the sources and
delivery chain of web ads and analyze performance considering the origin of the
ad contents. We observe that 2 of the well-known third-party ad domains
contribute to 35% of the ads performance cost and surprisingly, top news
websites implicitly include unknown third-party ads which in some cases build
up to more than 37% of the ads performance cost
A reputation framework for behavioural history: developing and sharing reputations from behavioural history of network clients
The open architecture of the Internet has enabled its massive growth and success by facilitating easy connectivity between hosts. At the same time, the Internet has also opened itself up to abuse, e.g. arising out of unsolicited communication, both intentional and unintentional. It remains an open question as to how best servers should protect themselves from malicious clients whilst offering good service to innocent clients. There has been research on behavioural profiling and reputation of clients, mostly at the network level and also for email as an application, to detect malicious clients. However, this area continues to pose open research challenges. This thesis is motivated by the need for a generalised framework capable of aiding efficient detection of malicious clients while being able to reward clients with behaviour profiles conforming to the acceptable use and other relevant policies. The main contribution of this thesis is a novel, generalised, context-aware, policy independent, privacy preserving framework for developing and sharing client reputation based on behavioural history. The framework, augmenting existing protocols, allows fitting in of policies at various stages, thus keeping itself open and flexible to implementation. Locally recorded behavioural history of clients with known identities are translated to client reputations, which are then shared globally. The reputations enable privacy for clients by not exposing the details of their behaviour during interactions with the servers. The local and globally shared reputations facilitate servers in selecting service levels, including restricting access to malicious clients. We present results and analyses of simulations, with synthetic data and some proposed example policies, of client-server interactions and of attacks on our model. Suggestions presented for possible future extensions are drawn from our experiences with simulation
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
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