A Generalized Evaluation Framework for Multimedia Recommender Systems

2018 ◽  
Vol 12 (04) ◽  
pp. 541-557 ◽  
Author(s):  
Mouzhi Ge ◽  
Fabio Persia
Keyword(s):  
Recommender Systems ◽  
Ad Hoc ◽  
Information Overload ◽  
Evaluation Criteria ◽  
Evaluation Framework ◽  
Multimedia Data ◽  
Media Technologies ◽  
Widespread Availability ◽  
Big Multimedia Data ◽  
Distinct Features

With the widespread availability of media technologies, such as real-time streaming, new Internet-of-Thing devices and smart phones, multimedia data are extensively increased and the big multimedia data rapidly spread over various social networks. This has created complexity and information overload for users to choose the suitable multimedia objects. Thus, different multimedia recommender systems have been emerging to help users find the useful multimedia objects that are possibly preferred by the user. However, the evaluation of these multimedia recommender systems is still in an ad-hoc stage. Given the distinct features of multimedia objects, the evaluation criteria adopted from the general recommender systems might not be effectively used to evaluate multimedia recommendations. In this paper, we therefore review and analyze the evaluation criteria that have been used in the previous multimedia recommender system papers. Based on the review, we propose a generalized evaluation framework to guide the researchers and practitioners to perform evaluations, especially user-centric evaluations, for multimedia recommender systems.

scholarly journals Information retrieval models for recommender systems

2019 ◽  
Vol 53 (1) ◽  
pp. 44-45
Author(s):  
Daniel Valcarce
Keyword(s):  
Information Retrieval ◽  
Recommender Systems ◽  
Relevance Feedback ◽  
Information Needs ◽  
Ad Hoc ◽  
Information Overload ◽  
Group Formation ◽  
Retrieval Models ◽  
Information Retrieval Evaluation ◽  
Pseudo Relevance Feedback

Information retrieval addresses the information needs of users by delivering relevant pieces of information but requires users to convey their information needs explicitly. In contrast, recommender systems offer personalized suggestions of items automatically. Ultimately, both fields help users cope with information overload by providing them with relevant items of information. This thesis aims to explore the connections between information retrieval and recommender systems. Our objective is to devise recommendation models inspired in information retrieval techniques. We begin by borrowing ideas from the information retrieval evaluation literature to analyze evaluation metrics in recommender systems [2]. Second, we study the applicability of pseudo-relevance feedback models to different recommendation tasks [1]. We investigate the conventional top-N recommendation task [5, 4, 6, 7], but we also explore the recently formulated user-item group formation problem [3] and propose a novel task based on the liquidation of long tail items [8]. Third, we exploit ad hoc retrieval models to compute neighborhoods in a collaborative filtering scenario [9, 10, 12]. Fourth, we explore the opposite direction by adapting an effective recommendation framework to pseudo-relevance feedback [13, 11]. Finally, we discuss the results and present our conclusions. In summary, this doctoral thesis adapts a series of information retrieval models to recommender systems. Our investigation shows that many retrieval models can be accommodated to deal with different recommendation tasks. Moreover, we find that taking the opposite path is also possible. Exhaustive experimentation confirms that the proposed models are competitive. Finally, we also perform a theoretical analysis of some models to explain their effectiveness. Advisors : Álvaro Barreiro and Javier Parapar. Committee members : Gabriella Pasi, Pablo Castells and Fidel Cacheda. The dissertation is available at: https://www.dc.fi.udc.es/~dvalcarce/thesis.pdf.

scholarly journals Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images

2021 ◽  
Author(s):  
Coleman R Harris ◽  
Eliot T McKinley ◽  
Joseph T Roland ◽  
Qi Liu ◽  
Martha J Shrubsole ◽  
...  
Keyword(s):  
Functional Data ◽  
Single Cell Analysis ◽  
Evaluation Criteria ◽  
Evaluation Framework ◽  
Complex Data ◽  
Imaging Data ◽  
Data Registration ◽  
Multiplexed Imaging ◽  
In Situ Methods ◽  
Clear Slide

The multiplexed imaging domain is a nascent single-cell analysis field with a complex data structure susceptible to technical variability that disrupts inference. These in situ methods are valuable in understanding cell-cell interactions, but few standardized processing steps or normalization techniques of multiplexed imaging data are available. We implement and compare data transformations and normalization algorithms in multiplexed imaging data. Our methods adapt the ComBat and functional data registration methods to remove slide effects in this domain, and we present an evaluation framework to compare the proposed approaches. We present clear slide-to-slide variation in the raw, unadjusted data, and show that many of the proposed normalization methods reduce this variation while preserving and improving the biological signal. Further, we find that dividing this data by its slide mean, and the functional data registration methods, perform the best under our proposed evaluation framework. In summary, this approach provides a foundation for better data quality and evaluation criteria in the multiplexed domain.

Recommender Technologies and Emerging Applications

2019 ◽  
pp. 458-470
Author(s):  
Young Park
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Future Research ◽  
Research Topics ◽  
Emerging Trends ◽  
System Approaches ◽  
Age Of Information

This chapter presents a brief overview of the field of recommender technologies and their emerging application domains. The authors explain the current major recommender system approaches within a unifying model, discuss emerging applications of recommender systems beyond traditional e-commerce, and outline emerging trends and future research topics, along with additional readings in the area of recommender technologies and applications. They believe that personalized recommender technologies will continue to advance and be applied in a variety of traditional and emerging application domains to assist users in the age of information overload.

Enhancing Citizens' Participation via Recommender Systems

2020 ◽  
pp. 624-650
Author(s):  
Luis Terán
Keyword(s):  
Decision Making ◽  
Semantic Web ◽  
Private Sector ◽  
Web 2.0 ◽  
Recommender Systems ◽  
Search Engines ◽  
Information Overload ◽  
Relevant Information ◽  
Research Topic ◽  
Government Services

With the introduction of Web 2.0, which includes users as content generators, finding relevant information is even more complex. To tackle this problem of information overload, a number of different techniques have been introduced, including search engines, Semantic Web, and recommender systems, among others. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. In this chapter, the use of recommender systems on eParticipation is presented. A brief description of the eGovernment Framework used and the participation levels that are proposed to enhance participation. The highest level of participation is known as eEmpowerment, where the decision-making is placed on the side of citizens. Finally, a set of examples for the different eParticipation types is presented to illustrate the use of recommender systems.

Toward Geospatial Collaborative Tourism Recommender Systems

2018 ◽  
pp. 212-248
Author(s):  
Zahra Bahramian ◽  
Rahim Ali Abbaspour ◽  
Christophe Claramunt
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
World Wide ◽  
Information Overload ◽  
Spatial Information ◽  
User Profile ◽  
User Preferences ◽  
Research Issues ◽  
The World ◽  
Information Finding

Tourism activities are highly dependent on spatial information. Finding the most interesting travel destinations and attractions and planning a trip are still open research issues to GIScience research applied to the tourism domain. Nowadays, huge amounts of information are available over the world wide web that may be useful in planning a visit to destinations and attractions. However, it is often time consuming for a user to select the most interesting destinations and attractions and plan a trip according to his own preferences. Tourism recommender systems (TRSs) can be used to overcome this information overload problem and to propose items taking into account the user preferences. This chapter reviews related topics in tourism recommender systems including different tourism recommendation approaches and user profile representation methods applied in the tourism domain. The authors illustrate the potential of tourism recommender systems as applied to the tourism domain by the implementation of an illustrative geospatial collaborative recommender system using the Foursquare dataset.

Towards an Evaluation Framework for E-health Services

2010 ◽  
pp. 1-16 ◽  
Author(s):  
Alalwany Hamid ◽  
Alshawi Sarmad
Keyword(s):  
Health Services ◽  
Critical Analysis ◽  
New Products ◽  
Evaluation Criteria ◽  
Evaluation Framework ◽  
Healthcare Organisation ◽  
Health Evaluation ◽  
Health Initiatives ◽  
Present Evaluation

The purpose of this study is to explore the user’s perspective in evaluating e-health services, and to present evaluation criteria that influence user’s utilization and satisfaction of e-health services. The evaluation criteria are based on two lines of studies relating to the behaviour of users of new products or services and on broad examining and critical analysis of the existing evaluations initiatives in e-health context. The evaluation criteria can serve as part of an e-health evaluation framework, and also to provide useful tools to allow the development of successful e-health initiatives by assisting the healthcare organisation to address areas that require further attention.

Clustering Approaches for Top-k Recommender Systems

2019 ◽  
Vol 28 (05) ◽  
pp. 1950019 ◽  
Author(s):  
Nicolás Torres ◽  
Marcelo Mendoza
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Large Scale ◽  
Empirical Studies ◽  
Cluster Structure ◽  
Parallel Architecture ◽  
Evaluation Framework ◽  
Training Data ◽  
Cluster Solution ◽  
Novel Approach

Clustering-based recommender systems bound the seek of similar users within small user clusters providing fast recommendations in large-scale datasets. Then groups can naturally be distributed into different data partitions scaling up in the number of users the recommender system can handle. Unfortunately, while the number of users and items included in a cluster solution increases, the performance in terms of precision of a clustering-based recommender system decreases. We present a novel approach that introduces a cluster-based distance function used for neighborhood computation. In our approach, clusters generated from the training data provide the basis for neighborhood selection. Then, to expand the search of relevant users, we use a novel measure that can exploit the global cluster structure to infer cluster-outside user’s distances. Empirical studies on five widely known benchmark datasets show that our proposal is very competitive in terms of precision, recall, and NDCG. However, the strongest point of our method relies on scalability, reaching speedups of 20× in a sequential computing evaluation framework and up to 100× in a parallel architecture. These results show that an efficient implementation of our cluster-based CF method can handle very large datasets providing also good results in terms of precision, avoiding the high computational costs involved in the application of more sophisticated techniques.

Workflow Management Systems in MANETs

2007 ◽  
pp. 1043-1049
Author(s):  
F. Rosa
Keyword(s):  
Ad Hoc Networks ◽  
Mobile Device ◽  
Ad Hoc ◽  
Workflow Management ◽  
Handheld Devices ◽  
Computing Environment ◽  
Emergency Situations ◽  
Military Applications ◽  
Widespread Availability ◽  
Hoc Networks

The widespread availability of network-enabled handheld devices (e.g., PDAs with WiFi) has made pervasive computing environment development an emerging reality. Mobile (or multi-hop) Ad-hoc NETworks (MANETs-Agrawal & Zeng, 2003) are mobile device networks communicating via wireless links without relying on an underlying infrastructure. Each device in a MANET acts as an endpoint and as a router forwarding messages to devices within radio range. MANETs are a sound alternative to infrastructure-based networks whenever the infrastructure is lacking or unusable, for example, military applications, disaster/relief, emergency situations, and communication between vehicles.

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