A highly adaptive recommender system based on fuzzy logic for B2C e-commerce portals

Castro-Schez, J. J.; Miguel, R.; Vallejo, David; Lopez-Lopez, L. M.

doi:10.1016/j.eswa.2010.08.033

Cited by 81 publications

(30 citation statements)

References 25 publications

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“…However, the approach manages objects arrangement and product catalogue using lexicographical descriptions, which are prone to returning irrelevant results because of polysemy of words, and its inability to use current technology for automatic comparisons across product recommendations, while the approach is suitable for C2C, its inappropriate for a direct shopping paradigm such as B2C (Business-to-Consumer) e-commerce. In bridging the gap, the authors of [54] proposed a B2C paradigm that hierarchically arranges products according to their features and use a product selection system to search and return another list of relevant products to be presented to the users as the set of recommendations. Whereas the authors of [54] consider a generic B2C scenario, the authors of [55] consider a scenario where the consumers can be physically present at the shopping mall popularly known as In-Store-Shopping (ISS).…”

Section: (1) E-commerce Domainmentioning

confidence: 99%

“…In bridging the gap, the authors of [54] proposed a B2C paradigm that hierarchically arranges products according to their features and use a product selection system to search and return another list of relevant products to be presented to the users as the set of recommendations. Whereas the authors of [54] consider a generic B2C scenario, the authors of [55] consider a scenario where the consumers can be physically present at the shopping mall popularly known as In-Store-Shopping (ISS).…”

Section: (1) E-commerce Domainmentioning

confidence: 99%

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Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

et al. 2017

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Intelligent data handling techniques are beneficial for users; to store, process, analyze and access the vast amount of information produced by electronic and automated devices. The leading approach is to use recommender systems (RS) to extract relevant information from the vast amount of knowledge. However, early recommender systems emerged without the cognizance to contextualize information regarding users' recommendations. Considering the historical methodological limitations, Context-Aware Recommender Systems (CARS) are now deployed, which leverage contextual information in addition to the classical two-dimensional search processes, providing better-personalized user recommendations. This paper presents a review of recent developmental processes as a fountainhead for the research of a context-aware recommender system. This work contributes by taking an integrated approach to the complete CARS developmental process, unlike other review papers, which only address a specific aspect of the CARS process. First, an in-depth review is presented pertaining to the state-of-the-art and classified literature, considering the domain of the application models, filters, extraction and evaluation approaches. Second, viewpoints are presented relating to the extraction of literature with analysis on the merit and demerit of each, and the evolving processes between them. Finally, the outstanding challenges and opportunities for future research directions are highlighted.

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Section: (1) E-commerce Domainmentioning

confidence: 99%

Section: (1) E-commerce Domainmentioning

confidence: 99%

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

et al. 2017

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“…The experiments will be carried out using MovieLens and Netflix datasets. papers are focused on movie recommendation studies (Winoto & Tang, 2010;CarrerNeto, Hernández-Alcaraz, Valencia-García, & García-Sánchez, 2012); however, a great volume of literature for RS is centered on different topics, such as music (Lee, Cho, & Kim, 2010;Nanolopoulos, Rafailidis, Symeonidis, & Manolopoulos, 2010;Tan, Bu, Chen, & He, 2011), television (Yo, Zhou, Hao, & Gu, 2006;Barragáns-Martínez, Costa-Montenegro, Burguillo, Rey-López, Mikic-Fonte, & Peleteiro, 2010), books (Goldberg, Roeder, Gupta, & Perkins, 2001; Núñez-Valdés, Cueva-Lovelle, Sanjuán-Martínez, Ordoñez de Pablos, & Monegro Marín, 2012), documents (Porcel, Moreno, & Herrera-Viedma, 2009;Porcel & Herrera-Viedma, 2010;Serrano-Guerrero, HerreraViedma, Olivas, Cerezo, & Romero, 2011;Porcel, Tejada-Lorente, Martínez, & Herrera-Viedma, 2012), e-learning (Zaiane, 2002;Bobadilla, Serradilla, & Hernando, 2009), e-commerce (Huang, Zeng, & Chen, 2007;Castro-Schez, Miguel, Vallejo, & López-López, 2011), applications in markets (Costa-Montenegro, Barragáns-Martínez, & Rey-López, 2012), tourism (Borràs, Moreno, & Valls, 2014) and web search (McNally, O'mahony, Coyle, Briggs, & Smyth, 2011;Zhang, Yu, Fang, You, Liu, Liu, & Yan, 2014), among others.…”

Section: Introductionmentioning

confidence: 99%

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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“…This observation is not specific to the domain of movies. This issue may be found in every domain when a sentiment analysis is done as in politics ( [1]), e-commerce ( [2]) or recommender systems ( [3]). ..…”

Section: Introductionmentioning

confidence: 99%

Opinion Extraction Applied to Criteria

Duthil¹,

Trousset²,

Dray³

et al. 2012

Lecture Notes in Computer Science

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Abstract. The success of Information technologies and associated services (e.g., blogs, forums,...) eases the way to express massive opinion on various topics. Recently new techniques known as opinion mining have emerged. One of their main goals is to automatically extract a global trend from expressed opinions. While it is easy to get this overall assessment, a more detailed analysis will highlight that the opinions are expressed on more specific topics: one will acclaim a movie for its soundtrack and another will criticize it for its scenario. Opinion mining approaches have little explored this multicriteria aspect. In this paper we propose an automatic extraction of text segments related to a set of criteria. The opinion expressed in each text segment is then automatically extracted. From a small set of opinion keywords, our approach automatically builds a training set of texts from the web. A lexicon reflecting the polarity of words is then extracted from this training corpus. This lexicon is then used to compute the polarity of extracted text segments. Experiments show the efficiency of our approach.

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A highly adaptive recommender system based on fuzzy logic for B2C e-commerce portals

Cited by 81 publications

References 25 publications

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

Incorporating group recommendations to recommender systems: Alternatives and performance

Opinion Extraction Applied to Criteria

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