Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation

Hurley, Neil; Zhang, Mi

doi:10.1145/1944339.1944341

Cited by 263 publications

(159 citation statements)

References 24 publications

(35 reference statements)

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“…Another way of deriving the suboptimal item set R is through the use of binary quadratic programming [31], [34].…”

Section: Fig 2 Modification In Cf Methodsmentioning

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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“…Another way of deriving the suboptimal item set R is through the use of binary quadratic programming [31], [34].…”

Section: Fig 2 Modification In Cf Methodsmentioning

confidence: 99%

A Survey on Recommendation Methods Beyond Accuracy

Han

Yamana

2017

IEICE Trans. Inf. & Syst.

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“…(d) Diversity metrics: such as the diversity and the novelty of the recommended items (Hurley & Zhang, 2011). Hernández and Gaudioso (2008) propose an evaluation process based on the distinction between interactive and non-interactive subsystems.…”

Section: Evaluation Methods Overviewmentioning

confidence: 99%

“…To evaluate these aspects, various metrics have been proposed to measure recommendation novelty and diversity (Hurley & Zhang, 2011;Vargas& Castells, 2011). …”

Section: Evaluation Methods Overviewmentioning

confidence: 99%

“…Overspecialization problem refers to the phenomenon in which users only receive recommendations for items that are very similar to items they liked or preferred; therefore, the users are not receiving recommendations for items that they might like but are unknown (e.g., when an user only receives recommendations about fiction films). Recommendations can be evaluated for novelty (Bobadilla, Hernando, Ortega, & Bernal, 2011;Hurley& Zhang, 2011).…”

Section: Content Based Filteringmentioning

confidence: 99%

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Incorporating group recommendations to recommender systems: Alternatives and performance

Ortega

Bobadilla

Hernando

et al. 2013

Information Processing & Management

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Proporcionaré una formalización completa del método propuesto. Explicaré cómo obtener el conjunto de k vecinos del grupo de usuarios y mostraré cómo obtener recomendaciones usando dichos vecinos. Asimismo, incluiré un ejemplo detallando cada paso del método propuesto en un sistema de recomendación compuesto por 8 usuarios y 10 items.Las principales características del método propuesto son: (a) es más rápido (más eficiente) que las alternativas proporcionadas por otros autores, y (b) es al menos tan exacto y preciso como otras soluciones estudiadas. Para contrastar esta hipótesis realizaré varios experimentos que miden la precisión, la exactitud y el rendimiento del método. Los resultados obtenidos se compararán con los resultados de otras alternativas utilizadas en la recomendación de grupos. Los experimentos se realizarán con las bases de datos de MovieLens y Netflix. VIII IX ABSTRACTThe importance of recommender systems has grown exponentially with the advent of social networks. In this PhD thesis I will provide a wide vision about the state of the art of recommender systems. They were initially based on demographic, contentbased and collaborative filtering. Currently, these systems incorporate some social information to the recommendation process. In the future, they will use implicit, local and personal information from the Internet of Things.As we will see here, recommender systems based on collaborative filtering can be used to perform recommendations to group of users. Previous works have made this modification in different stages of the collaborative filtering algorithm: establishing the neighborhood, prediction phase and determination of recommended items. In thisPhD thesis I will provide a new method that carry out the unification process (many users to one group) in the first stage of the collaborative filtering algorithm: similarity metric computation.I will provide a full formalization of the proposed method. I will explain how to obtain the k nearest neighbors of the group of users and I will show how to get recommendations using those users. I will also include a running example of a recommender system with 8 users and 10 items detailing all the steps of the method I will present.The main highlights of the proposed method are: (a) it will be faster (more efficient) that the alternatives provided by other authors, and (b) it will be at least as precise and accurate as other studied solutions. To check this hypothesis I will conduct several experiments measuring the accuracy, the precision and the performance of my method. I will compare these results with the results generated by other methods of group recommendation. The experiments will be carried out using MovieLens and Netflix datasets. The kinds of filtering most used at the beginning of the RS (collaborative, content-based and demographic) were described in Pazzani (1999). Breese,

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“…The basic idea is that the music that is at the end of the popularity of the popularity is more likely to make the user feel new. Assuming that S represents a set of users, the novelty definition of user u's recommendation list is as follows [7]:…”

Section: Advances In Intelligent Systems Research Volume 147mentioning

confidence: 99%

A Survey of Music Personalized Recommendation System

Xie¹,

Ding²

2018

Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

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Abstract. The study of music recommendation research was conducted and the corresponding research hotspots were proposed. The literature analysis method was used to briefly introduce the recommendation strategy from the perspective of the recommendation algorithm. It further proposes a method of using rough set theory to extract important context information and combines this user preference in context with collaborative filtering recommendation technology to implement a new idea of context recommendation-based music recommendation. The existing studies have a lack of systematic research on user behaviors and needs, low levels of feature extraction, and single problems in evaluation indicators. In the future, more in-depth discussions can be conducted in terms of group music recommendation, ontology modeling, and contextual awareness-based music recommendation in a mobile environment.

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Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation

Cited by 263 publications

References 24 publications

A Survey on Recommendation Methods Beyond Accuracy

A Survey on Recommendation Methods Beyond Accuracy

Incorporating group recommendations to recommender systems: Alternatives and performance

A Survey of Music Personalized Recommendation System

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