Recommendation system based contextual analysis of Facebook comment

Kharrat, Firas Ben; Elkhleifi, Aymen; Faiz, Rim

doi:10.1109/aiccsa.2016.7945792

Cited by 8 publications

(7 citation statements)

References 19 publications

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“…The group with papers [19,[93][94][95][96] focuses on analyzing user's preference extracted from opinion mining and on the use of this information as context-dependent preferences. The group with papers [91,100,102] has as a differential to deal with the cold start problem in context-aware recommendation systems by using opinion mining to obtain an initial model when there is not enough information for collaborative filtering. Finally, the group with papers [101,104] explores ontologies and language models to support opinion mining in the context-aware recommendation systems.…”

Section: Reference Opinion Information How Opinion Information Is Extmentioning

confidence: 99%

“…The extraction of the tags depends on a pre-defined list of item tags and the analysis of feelings for each tag is done by using a dictionary of opinion words built based on WordNet. Automating the extraction of the tags, which would discard the need of manually defining a set of tags, would improve the method of Kharrat et al [100]. In addition, user opinion is considered as its context, not taking into account other types of contextual information.…”

Section: The Second Part Relies On Three Fundamental Aspectsmentioning

confidence: 99%

“…Kharrat et al [100] proposed using external resource, i.e., Facebook comments, to mitigate the cold start problem and to improve the recommendation. The proposed approach consists of three parts:…”

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confidence: 99%

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Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review

et al. 2019

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Recommender systems help users by recommending items, such as products and services, that can be of interest to these users. Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user’s current context (e.g., location and time). Moreover, the advent of Web 2.0 and the growing popularity of social and e-commerce media sites have encouraged users to naturally write texts describing their assessment of items. There are increasing efforts to incorporate the rich information embedded in user’s reviews/texts into the recommender systems. Given the importance of this type of texts and their usage along with opinion mining and contextual information extraction techniques for recommender systems, we present a systematic review on the recommender systems that explore both contextual information and opinion mining. This systematic review followed a well-defined protocol. Its results were based on 17 papers, selected among 195 papers identified in four digital libraries. The results of this review give a general summary of the current research on this subject and point out some areas that may be improved in future primary works.

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Section: Reference Opinion Information How Opinion Information Is Extmentioning

confidence: 99%

Section: The Second Part Relies On Three Fundamental Aspectsmentioning

confidence: 99%

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Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review

et al. 2019

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“…Social information the "credibility" of users [7], social relationships of users discovered by social networks [8] Social behaviors of users Users' browsing behaviors [9], users' point of interest [10] Opinions of users Comments given by users [11,12] Information of items Items' reputations, semantic contents [6] and items' attributes [5,13] Tag information Tags annotated by users and tags provided by systems [14] Beside the basic descriptions of users and items, tag information, which has been incorporated into hybrid CBF/CF algorithms by being used to calculate user-based and item-based similarity measures [14], is a kind of useful semantic information for recommendation systems.…”

Section: Categories Detailed Descriptionmentioning

confidence: 99%

An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems

et al. 2018

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Social tag information has been used by recommender systems to handle the problem of data sparsity. Recently, the relationships between users/items and tags are considered by most tag-induced recommendation methods. However, sparse tag information is challenging to most existing methods. In this paper, we propose an Extended-Tag-Induced Matrix Factorization technique for recommender systems, which exploits correlations among tags derived by co-occurrence of tags to improve the performance of recommender systems, even in the case of sparse tag information. The proposed method integrates coupled similarity between tags, which is calculated by the co-occurrences of tags in the same items, to extend each item's tags. Finally, item similarity based on extended tags is utilized as an item relationship regularization term to constrain the process of matrix factorization. MovieLens dataset and Book-Crossing dataset are adopted to evaluate the performance of the proposed algorithm. The results of experiments show that the proposed method can alleviate the impact of tag sparsity and improve the performance of recommender systems.

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“…Essas técnicas, em suas formas tradicionais, consideram apenas o conjunto de ratings, ou o conjunto de acessos dos usuários. Entretanto, estudos empíricos indicam que abordagens baseadas em contexto podem produzir recomendações mais precisas (KHARRAT; ELKHLEIFI; FAIZ, 2016;MISSAOUI et al, 2017;BARAL et al, 2018). Um sistema de recomendação de pacotes de viagem, por exemplo, pode melhorar o desempenho da recomendação considerando o contexto "estação do ano" no qual o usuário deseja viajar, já que alguns lugares são mais recomendados no contexto "verão" enquanto outros são mais recomendados no contexto "inverno".…”

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Extração de contexto de reviews para sistemas de recomendação utilizando mineração de textos e opiniões

Sundermann¹

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Pelo carinho, afeto, dedicação e cuidado que meus pais me deram durante toda a minha existência, dedico esta tese a eles. Com muita gratidão. AGRADECIMENTOSAgradeço a Deus por tantas oportunidades e pessoas maravilhosas colocadas em minha vida.Agradeço a minha família: meus pais Arnaldo e Cidinha, meu filho Kauê e meu noivo Febirson, pela força e motivação para nunca desistir, mesmo com todas as adversidades enfrentadas nesse período. E com tudo, agradeço pela paciência infinita.Agradeço as minhas amigas, quase irmãs, que só pela presença me confortam e me incentivam a não desanimar. Ivone, Roberta e Jéssica, foram muitos risos, choros, cafés e jogos para distrair. Além do inesquecível show do Coldplay.Agradeço imensamente minha orientadora. Sempre muito amiga e compreensível. Orientando além da vida acadêmica, também a vida pessoal. Dando conselhos e me motivando nas horas mais difíceis. Agradeço pela primeira aula na disciplina de Tópicos Avançados em IA. Foi nesse momento que descobri minha paixão por IA e ganhei uma orientadora de TCC, mestrado e doutorado.Agradeço ao Anand pelas orientações e ensinamentos profissionais e pessoais, além das revisões de artigos em inglês. Não poderia deixar de agradecer ao meu co-orientador Marcos Domingues. Devo muitos agradecimentos pela orientação, pelos ensinamentos e pelas parcerias.Agradeço aos parceiros e colaboradores: Roberta Sinoara, Ivone Matsuno, João Antunes, Ricardo Marcacini, Renan de Pádua, Vítor Tonon, entre outros que foram pessoas relevantes para a realização deste trabalho.Agradeço a todos os amigos do Labic pelos vários anos dividindo tantos momentos. Pelas parcerias, ajudas e risadas compartilhadas.Agradeço à Itera (Val e Marco) pela oportunidade e confiança e a todos meus amigos de trabalho que deixam meus dias mais leves.Agradeço ao ICMC -USP, funcionários e professores pelos quase 13 anos de estudos, aprendizados e crescimento pessoal e profissional.Por fim, mas não menos importante, agradeço à CAPES pelo auxílio financeiro que possibilitou o desenvolvimento deste doutorado e os resultados obtidos. uma técnica de extração de contexto baseada em word embeddings; (iii) uma técnica de extração de contexto baseada em regras de associação, a RulesContext; e (iv) uma técnica de extração de contexto baseada em mineração de opiniões no nível dos aspectos, a CEOM. Essas propostas foram avaliadas considerando a base de reviews Yelp, sistemas de recomendação baseados nos vizinhos mais próximos, sistemas de recomendação baseados em fatoração de matrizes e diferentes baselines. Os resultados demonstraram que o uso das informações extraídas pelas técnicas propostas levaram a geração de recomendações mais precisas.Palavras-chave: Contexto, Sistemas de Recomendação, Mineração de Textos, Mineração de Opiniões.ABSTRACT SUNDERMANN, C. V. Extraction of context from reviews for recommender systems using text and opinion mining. 2020. 153 p. Tese (Doutorado em Ciências -Today, with the information, service and product overload on the Web, users have found it difficult to id...

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Recommendation system based contextual analysis of Facebook comment

Cited by 8 publications

References 19 publications

Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review

Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review

An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems

Extração de contexto de reviews para sistemas de recomendação utilizando mineração de textos e opiniões

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