20 research outputs found
Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction
Recommendation plays an increasingly important role in our daily lives.
Recommender systems automatically suggest items to users that might be
interesting for them. Recent studies illustrate that incorporating social trust
in Matrix Factorization methods demonstrably improves accuracy of rating
prediction. Such approaches mainly use the trust scores explicitly expressed by
users. However, it is often challenging to have users provide explicit trust
scores of each other. There exist quite a few works, which propose Trust
Metrics to compute and predict trust scores between users based on their
interactions. In this paper, first we present how social relation can be
extracted from users' ratings to items by describing Hellinger distance between
users in recommender systems. Then, we propose to incorporate the predicted
trust scores into social matrix factorization models. By analyzing social
relation extraction from three well-known real-world datasets, which both:
trust and recommendation data available, we conclude that using the implicit
social relation in social recommendation techniques has almost the same
performance compared to the actual trust scores explicitly expressed by users.
Hence, we build our method, called Hell-TrustSVD, on top of the
state-of-the-art social recommendation technique to incorporate both the
extracted implicit social relations and ratings given by users on the
prediction of items for an active user. To the best of our knowledge, this is
the first work to extend TrustSVD with extracted social trust information. The
experimental results support the idea of employing implicit trust into matrix
factorization whenever explicit trust is not available, can perform much better
than the state-of-the-art approaches in user rating prediction
Recommender System Based on Expert and Item Category
The objective of this study was to introduce the recommender system based on expert and item category to match the right items to users. In this study, the expert identification was divided into 3 techniques which were 1) the experts from social network technique 2) the experts from the frequency of rating technique and 3) the experts from other user’s preferences. To filter the expert users by using the frequency of rating technique and the experts from other user’s preferences technique, data about item category is used. For evaluation in this study, the researcher used Epinion for the performance testing to find out errors and accuracies in the prediction process. The results of this study showed that all the presented techniques had mean absolute error score at 0.15 and 85 percentages of accuracy, especially the expert identification combining with item category, it can reduce 60 percentages of the duration of recommendation creatingThe objective of this study was to introduce the recommender system based on expert and item category to match the right items to users. In this study, the expert identification was divided into 3 techniques which were 1) the experts from social network technique, 2) the experts from the frequency of rating technique, and 3) the experts from other user’s preferences. To filter the expert users by using the frequency of rating technique and the experts from other user’s preferences technique, data about item category is used. For evaluation in this study, the researcher used Epinion for the performance testing to find out errors and accuracies in the prediction process. The results of this study showed that all the presented techniques had mean absolute error score at 0.15 and 85 percentages of accuracy, especially the expert identification combining with item category, it can reduce 60 percentages of the duration of recommendation creating
Relational Network of People Constructed on the Basis of Similarity of Brain Activities.
The relational network of people (RNP) model has been attracting the interest of not only researchers but also industrial engineers. RNP can be constructed from friend lists in online social networking services (SNSs) and from inter-contact logs between individuals. One of the killer applications of RNP is the prediction of user demands, which is key to maximizing user satisfaction in content delivery services such as video streaming and video advertising. It is well known that an RNP representing social closeness between individuals (a so-called social network) can estimate user preferences simply, as we expect that people close to each other will have similar preferences. However, although there are many metrics that enable the social closeness between individuals to be measured, it is unclear which metric is best suited for individual services. Therefore, this paper introduces a new approach based on brain imaging. Brain imaging using functional Magnetic Resonance Imaging (fMRI) is powerful because it enables us to directly observe how a video content stimulates the brains of individual people. We propose a brain imaging-based RNP that represents the similarity of video-evoked brain activities between people as a network graph. We show an application scenario featuring predictive content delivery using the proposed RNP in which, when a user shows interest in a video content in some way, other users close to him or her can be expected to also be interested in it because their brain activities are correlated. Through numerical evaluation using multiple real datasets obtained by fMRI, we demonstrate that the proposed RNP is generalizable across brain imaging results for different sets of video content, thus suggesting that brain imaging data can be used to robustly generate RNP for utilization as a powerful tool for estimating user preferences
Accumulative time-based ranking method to reputation evaluation in information networks
With the rapid development of modern technology, the Web has become an
important platform for users to make friends and acquire information. However,
since information on the Web is over-abundant, information filtering becomes a
key task for online users to obtain relevant suggestions. As most Websites can
be ranked according to users' rating and preferences, relevance to queries, and
recency, how to extract the most relevant item from the over-abundant
information is always a key topic for researchers in various fields. In this
paper, we adopt tools used to analyze complex networks to evaluate user
reputation and item quality. In our proposed accumulative time-based ranking
(ATR) algorithm, we incorporate two behavioral weighting factors which are
updated when users select or rate items, to reflect the evolution of user
reputation and item quality over time. We showed that our algorithm outperforms
state-of-the-art ranking algorithms in terms of precision and robustness on
empirical datasets from various online retailers and the citation datasets
among research publications
Learning Agent for a Service-Oriented Context-Aware Recommender System in Heterogeneous Environment
Traditional recommender systems provide users with customized recommendations of products or services. They employ various technologies and algorithms in order to search and select the best options available while taking into account the user's context. Increasingly often, such systems run on devices in heterogeneous environments (including mobile devices) making use of their functionalities: various sensors (e.g. movement, light), wireless data transmission technologies and positioning systems (e.g. GPS) among others. In this paper, we propose an innovative recommender system that determines the best service (including photo and movie conversion) and simultaneously accommodates the context of the device in a heterogeneous environment. The system allows the choice between various service providers that make their resources available using cloud computing as well as having the services performed locally. In order to determine the best possible recommendation for users, we employ the concept of learning agents, which has not been thoroughly researched in connection with recommender systems so far
The Public Service Approach to Recommender Systems : Filtering to Cultivate
Online media consumption has been radically transformed by how media companies algorithmically recommend content to their users. Public service media (PSM) have also realized the potential of recommender systems and are increasingly using these technologies to personalize their online offering. PSM are on the other hand required to disseminate diverse content, which can be incompatible with the logics of commercial recommender systems that primarily seek to drive up media consumption. Drawing on previous research on selective exposure and media diversity, this study presents the results from interviews with ten PSM informants across Europe, revealing that data scientists within these organizations are highly aware of the effects recommendations have on media consumption, and design the PSM online services accordingly. This study contributes with in-depth knowledge of how diversity has been interpreted at operational levels in PSM and how recommender systems are being adapted to a non-commercial setting.Peer reviewe
A Multi-Modal Latent-Features based Service Recommendation System for the Social Internet of Things
The Social Internet of Things (SIoT), is revolutionizing how we interact with
our everyday lives. By adding the social dimension to connecting devices, the
SIoT has the potential to drastically change the way we interact with smart
devices. This connected infrastructure allows for unprecedented levels of
convenience, automation, and access to information, allowing us to do more with
less effort. However, this revolutionary new technology also brings an eager
need for service recommendation systems. As the SIoT grows in scope and
complexity, it becomes increasingly important for businesses and individuals,
and SIoT objects alike to have reliable sources for products, services, and
information that are tailored to their specific needs. Few works have been
proposed to provide service recommendations for SIoT environments. However,
these efforts have been confined to only focusing on modeling user-item
interactions using contextual information, devices' SIoT relationships, and
correlation social groups but these schemes do not account for latent semantic
item-item structures underlying the sparse multi-modal contents in SIoT
environment. In this paper, we propose a latent-based SIoT recommendation
system that learns item-item structures and aggregates multiple modalities to
obtain latent item graphs which are then used in graph convolutions to inject
high-order affinities into item representations. Experiments showed that the
proposed recommendation system outperformed state-of-the-art SIoT
recommendation methods and validated its efficacy at mining latent
relationships from multi-modal features
Social Relations and Methods in Recommender Systems: A Systematic Review
With the constant growth of information, data sparsity problems, and cold start have become a complex problem in obtaining accurate recommendations. Currently, authors consider the user's historical behavior and find contextual information about the user, such as social relationships, time information, and location. In this work, a systematic review of the literature on recommender systems that use the information on social relationships between users was carried out. As the main findings, social relations were classified into three groups: trust, friend activities, and user interactions. Likewise, the collaborative filtering approach was the most used, and with the best results, considering the methods based on memory and model. The most used metrics that we found, and the recommendation methods studied in mobile applications are presented. The information provided by this study can be valuable to increase the precision of the recommendations