Pareto-efficient hybridization for multi-objective recommender systems

Ribeiro, Marco Túlio; Lacerda, Anísio; Veloso, Adriano; Ziviani, Nívio

doi:10.1145/2365952.2365962

Cited by 92 publications

(37 citation statements)

References 36 publications

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“…However, other metrics can also be used. For w a , most of the algorithms require manual tuning; however, some algorithms automatically adjust the weights by using a method such as a genetic program [8], [58]. As [14] points out, deriving the optimal item set R from D is NP-hard, as problems can be reduced to an existing NPhard problem, such as a set cover or maximum dispersion problem.…”

Section: Ob J(u R)mentioning

confidence: 99%

A Survey on Recommendation Methods Beyond Accuracy

Han

Yamana

2017

IEICE Trans. Inf. & Syst.

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SUMMARYIn recommending to another individual an item that one loves, accuracy is important, however in most cases, focusing only on accuracy generates less satisfactory recommendations. Studies have repeatedly pointed out that aspects that go beyond accuracy-such as the diversity and novelty of the recommended items-are as important as accuracy in making a satisfactory recommendation. Despite their importance, there is no global consensus about definitions and evaluations regarding beyondaccuracy aspects, as such aspects closely relate to the subjective sensibility of user satisfaction. In addition, devising algorithms for this purpose is difficult, because algorithms concurrently pursue the aspects in trade-off relation (i.e., accuracy vs. novelty). In the aforementioned situation, for researchers initiating a study in this domain, it is important to obtain a systematically integrated view of the domain. This paper reports the results of a survey of about 70 studies published over the last 15 years, each of which addresses recommendations that consider beyond-accuracy aspects. From this survey, we identify diversity, novelty, and coverage as important aspects in achieving serendipity and popularity unbiasedness-factors that are important to user satisfaction and business profits, respectively. The five major groups of algorithms that tackle the beyond-accuracy aspects are multi-objective, modified collaborative filtering (CF), clustering, graph, and hybrid; we then classify and describe algorithms as per this typology. The off-line evaluation metrics and user studies carried out by the studies are also described. Based on the survey results, we assert that there is a lot of room for research in the domain. Especially, personalization and generalization are considered important issues that should be addressed in future research (e.g., automatic per-user-trade-off among the aspects, and properly establishing beyond-accuracy aspects for various types of applications or algorithms).

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Section: Ob J(u R)mentioning

confidence: 99%

A Survey on Recommendation Methods Beyond Accuracy

Han

Yamana

2017

IEICE Trans. Inf. & Syst.

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“…Castells et al [5] introduce novelty and diversity as additional criteria. Ribeiro et al [23] emphasize that multiple criteria can be considered when learning suited reduction strategies. Said et al [24] elaborate on the need to consider non-functional criteria.…”

Section: News Recommendation Scenariomentioning

confidence: 99%

CLEF 2017 NewsREEL Overview: A Stream-Based Recommender Task for Evaluation and Education

Lommatzsch

Kille

Hopfgartner

et al. 2017

Lecture Notes in Computer Science

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Abstract. News recommender systems provide users with access to news stories that they find interesting and relevant. As other online, stream-based recommender systems, they face particular challenges, including limited information on users' preferences and also rapidly fluctuating item collections. In addition, technical aspects, such as response time and scalability, must be considered. Both algorithmic and technical considerations shape working requirements for realworld recommender systems in businesses. NewsREEL represents a unique opportunity to evaluate recommendation algorithms and for students to experience realistic conditions and to enlarge their skill sets. The NewsREEL Challenge requires participants to conduct data-driven experiments in NewsREEL Replay as well as deploy their best models into NewsREEL Live's 'living lab'. This paper presents NewsREEL 2017 and also provides insights into the effectiveness of NewsREEL to support the goals of instructors teaching recommender systems to students. We discuss the experiences of NewsREEL participants as well as those of instructors teaching recommender systems to students, and in this way, we showcase NewsREEL's ability to support the education of future data scientists.

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“…The [42] used evolutionary computing to find the optimal aggregation of different recommendation algorithms by considering multi-objectives such as ac- …”

Section: Approaches Based On Weights Optimizingmentioning

confidence: 99%

Recommender Systems Based on Evolutionary Computing: A Survey

Sadeghi¹,

Asghari²

2017

JSEA

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Data mining techniques and information personalization have made significant growth in the past decade. Enormous volume of data is generated every day. Recommender systems can help users to find their specific information in the extensive volume of information. Several techniques have been presented for development of Recommender System (RS). One of these techniques is the Evolutionary Computing (EC), which can optimize and improve RS in the various applications. This study investigates the number of publications, focusing on some aspects such as the recommendation techniques, the evaluation methods and the datasets which are used.

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Pareto-efficient hybridization for multi-objective recommender systems

Cited by 92 publications

References 36 publications

A Survey on Recommendation Methods Beyond Accuracy

A Survey on Recommendation Methods Beyond Accuracy

CLEF 2017 NewsREEL Overview: A Stream-Based Recommender Task for Evaluation and Education

Recommender Systems Based on Evolutionary Computing: A Survey

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