161 research outputs found
TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks
peer reviewedCollaborative filtering (CF) is a widely applied method to perform recommendation tasks in a wide range of domains and applications. Dictionary learning (DL) models, which are highly important in CF-based recommender systems (RSs), are well represented by rating matrices. However, these methods alone do not resolve the cold start and data sparsity issues in RSs. We observed a significant improvement in rating results by adding trust information on the social network. For that purpose, we proposed a new dictionary learning technique based on trust information, called TrustDL, where the social network data were employed in the process of recommendation based on structural details on the trusted network. TrustDL sought to integrate the sources of information, including trust statements and ratings, into the recommendation model to mitigate both problems of cold start and data sparsity. It conducted dictionary learning and trust embedding simultaneously to predict unknown rating values. In this paper, the dictionary learning technique was integrated into rating learning, along with the trust consistency regularization term designed to offer a more accurate understanding of the feature representation. Moreover, partially identical trust embedding was developed, where users with similar rating sets could cluster together, and those with similar rating sets could be represented collaboratively. The proposed strategy appears significantly beneficial based on experiments conducted on four frequently used datasets: Epinions, Ciao, FilmTrust, and Flixster
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A multi-scale framework for graph based machine learning problems
Graph data have become essential in representing and modeling relationships between entities and complex network structures in various domains such as social networks and recommender systems. As a main contributor of the recent Big Data trend, the massive scale of graphs in modern machine learning problems easily overwhelms existing methods and thus sophisticated scalable algorithms are needed for real-world applications. In this thesis, we develop a novel multi-scale framework based on the divide-and-conquer principle as an effective and scalable approach for machine learning tasks involving large sparse graphs. We first demonstrate how our multi-scale framework can be applied to the problem of computing the spectral decomposition of massive graphs, which is one of the most fundamental low-rank matrix approximations used in numerous machine learning tasks. While popular solvers suffer from slow convergence, especially when the desired rank is large, our method exploits the clustering structure of the graph and achieves superior performance compared to existing algorithms in terms of both accuracy and scalability. While the main goal of the divide-and-conquer approach is to efficiently compute solutions for the original problem, the proposed multi-scale framework further admits an attractive but less obvious feature that machine learning problems can benefit from. Particularly, we consider partial solutions of the subproblems computed in the process as localized models of the entire problem. By doing so, we can combine models at multiple scales from local to global and generate a holistic view of the underlying problem to achieve better performance than a single global view. We adapt such multi-scale view for the problems of link prediction in social networks and collaborative filtering in recommender systems with additional side information to obtain a model that can make accurate and robust predictions in a scalable manner.Computer Science
A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Recommender systems have significantly developed in recent years in parallel
with the witnessed advancements in both internet of things (IoT) and artificial
intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI,
multiple forms of data are incorporated in these systems, e.g. social,
implicit, local and personal information, which can help in improving
recommender systems' performance and widen their applicability to traverse
different disciplines. On the other side, energy efficiency in the building
sector is becoming a hot research topic, in which recommender systems play a
major role by promoting energy saving behavior and reducing carbon emissions.
However, the deployment of the recommendation frameworks in buildings still
needs more investigations to identify the current challenges and issues, where
their solutions are the keys to enable the pervasiveness of research findings,
and therefore, ensure a large-scale adoption of this technology. Accordingly,
this paper presents, to the best of the authors' knowledge, the first timely
and comprehensive reference for energy-efficiency recommendation systems
through (i) surveying existing recommender systems for energy saving in
buildings; (ii) discussing their evolution; (iii) providing an original
taxonomy of these systems based on specified criteria, including the nature of
the recommender engine, its objective, computing platforms, evaluation metrics
and incentive measures; and (iv) conducting an in-depth, critical analysis to
identify their limitations and unsolved issues. The derived challenges and
areas of future implementation could effectively guide the energy research
community to improve the energy-efficiency in buildings and reduce the cost of
developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl
GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
Given the convenience of collecting information through online services,
recommender systems now consume large scale data and play a more important role
in improving user experience. With the recent emergence of Graph Neural
Networks (GNNs), GNN-based recommender models have shown the advantage of
modeling the recommender system as a user-item bipartite graph to learn
representations of users and items. However, such models are expensive to train
and difficult to perform frequent updates to provide the most up-to-date
recommendations. In this work, we propose to update GNN-based recommender
models incrementally so that the computation time can be greatly reduced and
models can be updated more frequently. We develop a Graph Structure Aware
Incremental Learning framework, GraphSAIL, to address the commonly experienced
catastrophic forgetting problem that occurs when training a model in an
incremental fashion. Our approach preserves a user's long-term preference (or
an item's long-term property) during incremental model updating. GraphSAIL
implements a graph structure preservation strategy which explicitly preserves
each node's local structure, global structure, and self-information,
respectively. We argue that our incremental training framework is the first
attempt tailored for GNN based recommender systems and demonstrate its
improvement compared to other incremental learning techniques on two public
datasets. We further verify the effectiveness of our framework on a large-scale
industrial dataset.Comment: Accepted by CIKM2020 Applied Research Trac
Exploiting distributional semantics for content-based and context-aware recommendation
During the last decade, the use of recommender systems has been increasingly growing to the point that, nowadays, the success of many well-known services depends on these technologies. Recommenders Systems help people to tackle the choice overload problem by effectively presenting new content adapted to the user¿s preferences. However, current recommendation algorithms commonly suffer from data sparsity, which refers to the incapability of producing acceptable recommendations until a minimum amount of users¿ ratings are available for training the prediction models.
This thesis investigates how the distributional semantics of concepts describing the entities of the recommendation space can be exploited to mitigate the data-sparsity problem and improve the prediction accuracy with respect to state-of-the-art recommendation techniques. The fundamental idea behind distributional semantics is that concepts repeatedly co-occurring in the same context or usage tend to be related. In this thesis, we propose and evaluate two novel semantically-enhanced prediction models that address the sparsity-related limitations: (1) a content-based approach, which exploits the distributional semantics of item¿s attributes during item and user-profile matching, and (2) a context-aware recommendation approach that exploits the distributional semantics of contextual conditions during context modeling. We demonstrate in an exhaustive experimental evaluation that the proposed algorithms outperform state-of-the-art ones, especially when data are sparse.
Finally, this thesis presents a recommendation framework, which extends the widespread machine learning library Apache Mahout, including all the proposed and evaluated recommendation algorithms as well as a tool for offline evaluation and meta-parameter optimization. The framework has been developed to allow other researchers to reproduce the described evaluation experiments and make new progress on the Recommender Systems field easierDurant l'última dècada, l'ús dels sistemes de recomanació s'ha vist incrementat fins al punt que, actualment, l'èxit de molts dels serveis web més coneguts depèn en aquesta tecnologia. Els Sistemes de Recomanació ajuden als usuaris a trobar els productes o serveis que més s¿adeqüen als seus interessos i preferències. Una gran limitació dels algoritmes de recomanació actuals és el problema de "data-sparsity", que es refereix a la incapacitat d'aquests sistemes de generar recomanacions precises fins que un cert nombre de votacions d'usuari és disponible per entrenar els models de predicció. Per mitigar aquest problema i millorar així la precisió de predicció de les tècniques de recomanació que conformen l'estat de l'art, en aquesta tesi hem investigat diferents maneres d'aprofitar la semàntica distribucional dels conceptes que descriuen les entitats que conformen l'espai del problema de la recomanació, principalment, els objectes a recomanar i la informació contextual. En la semàntica distribucional s'assumeix la següent hipotesi: conceptes que coincideixen repetidament en el mateix context o ús tendeixen a estar semànticament relacionats. Concretament, en aquesta tesi hem proposat i avaluat dos algoritmes de recomanació que fan ús de la semàntica distribucional per mitigar el problem de "data-sparsity": (1) un model basat en contingut que explota les similituds distribucionals dels atributs que representen els objectes a recomanar durant el càlcul de la correspondència entre els perfils d'usuari i dels objectes; (2) un model de recomanació contextual que fa ús de les similituds distribucionals entre condicions contextuals durant la representació del context. Mitjançant una avaluació experimental exhaustiva dels models de recomanació proposats hem demostrat la seva efectivitat en situacions de falta de dades, confirmant que poden millorar la precisió d'algoritmes que conformen l'estat de l'art. Finalment, aquesta tesi presenta una llibreria pel desenvolupament i avaluació d'algoritmes de recomanació com una extensió de la llibreria de "Machine Learning" Apache Mahout, àmpliament utilitzada en el camp del Machine Learning. La nostra extensió inclou tots els algoritmes de recomanació avaluats en aquesta tesi, així com una eina per facilitar l'avaluació experimental dels algoritmes. Hem desenvolupat aquesta llibreria per facilitar a altres investigadors la reproducció dels experiments realitzats i, per tant, el progrés en el camp dels Sistemes de Recomanació
Trust and Credibility in Online Social Networks
Increasing portions of people's social and communicative activities now take place in the digital world. The growth and popularity of online social networks (OSNs) have tremendously facilitated online interaction and information exchange. As OSNs enable people to communicate more effectively, a large volume of user-generated content (UGC) is produced daily. As UGC contains valuable information, more people now turn to OSNs for news, opinions, and social networking. Besides users, companies and business owners also benefit from UGC as they utilize OSNs as the platforms for communicating with customers and marketing activities. Hence, UGC has a powerful impact on users' opinions and decisions. However, the openness of OSNs also brings concerns about trust and credibility online. The freedom and ease of publishing information online could lead to UGC with problematic quality. It has been observed that professional spammers are hired to insert deceptive content and promote harmful information in OSNs. It is known as the spamming problem, which jeopardizes the ecosystems of OSNs. The severity of the spamming problem has attracted the attention of researchers and many detection approaches have been proposed. However, most existing approaches are based on behavioral patterns. As spammers evolve to evade being detected by faking normal behaviors, these detection approaches may fail. In this dissertation, we present our work of detecting spammers by extracting behavioral patterns that are difficult to be manipulated in OSNs. We focus on two scenarios, review spamming and social bots. We first identify that the rating deviations and opinion deviations are invariant patterns in review spamming activities since the goal of review spamming is to insert deceptive reviews. We utilize the two kinds of deviations as clues for trust propagation and propose our detection mechanisms. For social bots detection, we identify the behavioral patterns among users in a neighborhood is difficult to be manipulated for a social bot and propose a neighborhood-based detection scheme. Our work shows that the trustworthiness of a user can be reflected in social relations and opinions expressed in the review content. Besides, our proposed features extracted from the neighborhood are useful for social bot detection
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