Comparing Pre-filtering and Post-filtering Approach in a Collaborative Contextual Recommender System: An Application to E-Commerce

Panniello, Umberto; Gorgoglione, Michele; Palmisano, Cosimo

doi:10.1007/978-3-642-03964-5_32

Cited by 8 publications

(8 citation statements)

References 11 publications

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“…As noted by [2], context-aware recommendation is a relatively unexplored area, and still needs a much better comprehension. The most notable work in comparing CARS approaches correspond to the series of studies from Panniello et al [9,[13][14][15]. They compare CARS approaches using heuristic-based CF algorithms.…”

Section: Related Workmentioning

confidence: 99%

Context-Aware Movie Recommendations: An Empirical Comparison of Pre-filtering, Post-filtering and Contextual Modeling Approaches

Campos

Fernández-Tobías

Cantador

et al. 2013

Lecture Notes in Business Information Processing

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract. Context-aware recommender systems have been proven to improve the performance of recommendations in a wide array of domains and applications. Despite individual improvements, little work has been done on comparing different approaches, in order to determine which of them outperform the others, and under what circumstances. In this paper we address this issue by conducting an empirical comparison of several pre-filtering, post-filtering and contextual modeling approaches on the movie recommendation domain. To acquire confident contextual information, we performed a user study where participants were asked to rate movies, stating the time and social companion with which they preferred to watch the rated movies. The results of our evaluation show that there is neither a clear superior contextualization approach nor an always best contextual signal, and that achieved improvements depend on the recommendation algorithm used together with each contextualization approach. Nonetheless, we conclude with a number of cues and advices about which particular combinations of contextualization approaches and recommendation algorithms could be better suited for the movie recommendation domain.

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Section: Related Workmentioning

confidence: 99%

Context-Aware Movie Recommendations: An Empirical Comparison of Pre-filtering, Post-filtering and Contextual Modeling Approaches

Campos

Fernández-Tobías

Cantador

et al. 2013

Lecture Notes in Business Information Processing

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“…For instance, analyzing which are the characteristics and values of distinct contextual signals -alone or in combination-that really influence recommendation performance improvements is an important open research issue. Some researchers have conducted studies on context-aware recommendation comparing different approaches [13,14,15], but little work has been done at the contextual signal level. Moreover, in general, reported studies have focused on individual domains, without analyzing the generalization of the proposed approaches for several domains.…”

Section: Introductionmentioning

confidence: 99%

A Contextual Modeling Approach for Model-Based Recommender Systems

Fernández-Tobías

Campos

Cantador

et al. 2013

Advances in Artificial Intelligence

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract. In this paper we present a contextual modeling approach for modelbased recommender systems that integrates and exploits both user preferences and contextual signals in a common vector space. Differently to previous work, we conduct a user study acquiring and analyzing a variety of realistic contextual signals associated to user preferences in several domains. Moreover, we report empirical results evaluating our approach in the movie and music domains, which show that enhancing model-based recommender systems with time, location and social companion information improves the accuracy of generated recommendations.

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“…The results also show that contexts play an essential role in personalisation and companies can utilise the opportunity to enhance the predictive performance of customer"s behaviour. The authors in [40] target to identify the effect of contextual information on recommendation performance. In doing so, they utilised the collaborative recommender system to compare a pre-filtering method to a post-filtering approach.…”

Section: Comparisons Across Contextual Pre-filtering Contextual Postmentioning

confidence: 99%

“…While [34,37,[39][40][41][42][43][44][45][46] performed the comparisons based on accuracy alone, [47] put into consideration also the diversity of recommendation to compare the several pre-filtering, postfiltering, and contextual modelling approaches to determine which method is superior to others and under which situation.…”

Section: Comparisons Across Contextual Pre-filtering Contextual Postmentioning

confidence: 99%

A Comprehensive Survey on Comparisons across Contextual Pre-filtering, Contextual Post-filtering and Contextual Modelling Approaches

et al. 2017

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Recently, there has been growing interest in recommender systems (RS) and particularly in context-aware RS. Methods for generating context-aware recommendations are classified into pre-filtering, post-filtering and contextual modelling approaches. In this paper, we present the several novel approaches of the different variant of each of these three contextualization paradigms and present a complete survey on the state-of-the-art comparisons across them. We then identify the significant challenges that requirebeing addressed by the current RS researchers, which will help academicians and practitioners in comparing these three approaches to select the best alternative according to their strategies.

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Comparing Pre-filtering and Post-filtering Approach in a Collaborative Contextual Recommender System: An Application to E-Commerce

Cited by 8 publications

References 11 publications

Context-Aware Movie Recommendations: An Empirical Comparison of Pre-filtering, Post-filtering and Contextual Modeling Approaches

Context-Aware Movie Recommendations: An Empirical Comparison of Pre-filtering, Post-filtering and Contextual Modeling Approaches

A Contextual Modeling Approach for Model-Based Recommender Systems

A Comprehensive Survey on Comparisons across Contextual Pre-filtering, Contextual Post-filtering and Contextual Modelling Approaches

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