scholarly journals A Guideline-Based Approach for Assisting with the Reproducibility of Experiments in Recommender Systems Evaluation

2019 ◽  
Vol 28 (08) ◽  
pp. 1960011
Author(s):  
Nikolaos Polatidis ◽  
Elias Pimenidis ◽  
Andrew Fish ◽  
Stelios Kapetanakis
Keyword(s):  
Information Retrieval ◽  
Recommender Systems ◽  
Predictive Accuracy ◽  
Evaluation Framework ◽  
Real Dataset ◽  
Design And Implementation ◽  
Comparison Of Results ◽  
Recommendation Algorithms ◽  
New Algorithms ◽  
Optimal Settings

Recommender systems’ evaluation is usually based on predictive accuracy and information retrieval metrics, with better scores meaning recommendations are of higher quality. However, new algorithms are constantly developed and the comparison of results of algorithms within an evaluation framework is difficult since different settings are used in the design and implementation of experiments. In this paper, we propose a guidelines-based approach that can be followed to reproduce experiments and results within an evaluation framework. We have evaluated our approach using a real dataset, and well-known recommendation algorithms and metrics; to show that it can be difficult to reproduce results if certain settings are missing, thus resulting in more evaluation cycles required to identify the optimal settings.

Managing Project Sustainability

2016 ◽  
pp. 708-732
Author(s):  
Gilman C. K. Tam
Keyword(s):  
Life Cycle ◽  
Assessment Tool ◽  
Project Managers ◽  
Evaluation Framework ◽  
Design And Implementation ◽  
Project Life Cycle ◽  
Research Findings ◽  
Project Sustainability ◽  
Key Barrier ◽  
Project Life

Managing project sustainability is becoming important in the last two decades since the Earth Summit in 1992. An increasing number of projects have built in sustainability considerations into project design and implementation. Recent research findings show that lack of sustainability knowledge for project managers is a key barrier to drive projects and programs contributing towards a sustainable society. Definitions and approaches (pillar-based and principles-based) to sustainability in project management together with project manager competence requirements are discussed. The purpose of this chapter is to devise an assessment tool for project managers incorporating the concept of pillar-based and principles-based sustainability approaches as well as the EIA-driven and objectives-led assessment methodologies. Criteria for selecting assessment scheme appropriate to various project initiatives are developed. Integrating selected assessment methodology into sustainability evaluation framework within the project life cycle forms a complete tool. This chapter contributes to devising a practical assessment tool for project managers in managing project sustainability.

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.

Exploring multiple diversification strategies for academic citation contexts recommendation

2020 ◽  
Vol 38 (4) ◽  
pp. 821-842
Author(s):  
Haihua Chen ◽  
Yunhan Yang ◽  
Wei Lu ◽  
Jiangping Chen
Keyword(s):  
Information Retrieval ◽  
Information Needs ◽  
Semantic Distance ◽  
Evaluation Framework ◽  
Data Set ◽  
Content Type ◽  
User Interests ◽  
Information Retrieval Evaluation ◽  
Diversification Strategies ◽  
Citation Contexts

Purpose Citation contexts have been found useful in many scenarios. However, existing context-based recommendations ignored the importance of diversity in reducing the redundant issues and thus cannot cover the broad range of user interests. To address this gap, the paper aims to propose a novelty task that can recommend a set of diverse citation contexts extracted from a list of citing articles. This will assist users in understanding how other scholars have cited an article and deciding which articles they should cite in their own writing. Design/methodology/approach This research combines three semantic distance algorithms and three diversification re-ranking algorithms for the diversifying recommendation based on the CiteSeerX data set and then evaluates the generated citation context lists by applying a user case study on 30 articles. Findings Results show that a diversification strategy that combined “word2vec” and “Integer Linear Programming” leads to better reading experience for participants than other diversification strategies, such as CiteSeerX using a list sorted by citation counts. Practical implications This diversifying recommendation task is valuable for developing better systems in information retrieval, automatic academic recommendations and summarization. Originality/value The originality of the research lies in the proposal of a novelty task that can recommend a diversification context list describing how other scholars cited an article, thereby making citing decisions easier. A novel mixed approach is explored to generate the most efficient diversifying strategy. Besides, rather than traditional information retrieval evaluation, a user evaluation framework is introduced to reflect user information needs more objectively.

A Database Project in a Small Company (or How the Real World Doesn't Always Follow the Book)

2009 ◽  
pp. 468-483
Author(s):  
Efrem Mallach
Keyword(s):  
Information Retrieval ◽  
Real World ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Small Company ◽  
The Real ◽  
Design And Implementation ◽  
Development Methods

The case study describes a small consulting company’s experience in the design and implementation of a database and associated information retrieval system. Their choices are explained within the context of the firm’s needs and constraints. Issues associated with development methods are discussed, along with problems that arose from not following proper development disciplines.

scholarly journals Design and Implementation of a Multimedia-based Technology Solution to Assist Children with Intellectual Disability to Learn

2017 ◽  
Vol 12 (04) ◽  
pp. 141
Author(s):  
Samir Abou El-Seoud ◽  
Jihad Alja'am ◽  
Masoud Mwinyi
Keyword(s):  
Intellectual Disability ◽  
Personalized Learning ◽  
Technological Solution ◽  
Design And Implementation ◽  
New Information ◽  
Words And Images ◽  
Main Ideas ◽  
New Algorithms ◽  
Technology Solution ◽  
Children With Intellectual Disability

This paper presents an innovative technological solution to improve the understanding and cognitive functions of children with intellectual disability through multimedia. New algorithms that mine the story-based scripts, rank their sentences, extract keywords and match them with multimedia have been proposed in our previous work. They are integrated in the proposed system and being assessed over 50 stories for children. The results showed that the children concentration and refection are highly improved using multimedia. The system is fully dynamic and support the personalized learning. Instructors can focus on the main ideas of the story which are extracted automatically and use then images and clips to explain them. The words and images are stored in a corpus which is enriched continuously. Every new story taught will add new information to the corpus and to every children dataset. Parents can review the tutorials used in school with their children at home. The system contributes to the reintegration of children with intellectual disability into the society and break their marginalization and isolation.

Toward Social-Semantic Recommender Systems

2016 ◽  
Vol 7 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Dalia Sulieman ◽  
Maria Malek ◽  
Hubert Kadima ◽  
Dominique Laurent
Keyword(s):  
Social Network ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Semantic Information ◽  
Basic Problem ◽  
Real Data ◽  
Data Sets ◽  
The Social ◽  
Recommendation Algorithms ◽  
Hybrid Recommendation

In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to avoid scanning the whole user set in order to produce the recommendation list. To cope with problem, they consider that users are connected through a social network and that taxonomy over the items has been defined. These two kinds of information are respectively called social and semantic information. In their contribution the authors suggest combining social information with semantic information in one algorithm in order to compute recommendation lists by visiting a limited part of the social network. In their experiments, the authors use two real data sets, namely Amazon.com and MovieLens, and they compare their algorithms with the standard item-based collaborative filtering and hybrid recommendation algorithms. The results show satisfying accuracy values and a very significant improvement of performance, by exploring a small part of the graph instead of exploring the whole graph.

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