Hybrid Recommender System Using Latent Features

Maneeroj, Saranya; Takasu, Atsuhiro

doi:10.1109/waina.2009.122

Cited by 16 publications

(10 citation statements)

References 8 publications

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“…In the experiments, we successfully extracted a set of sensible topics from short documents like abstracts, as well as the conventional methods on topic modeling [17], [24]. LDA has also been used to analyze other short documents, e.g., IMDB movie plots or reviews [30], [31]. However, some papers argued that extracting meaningful topics from short documents sometimes fails due to the lack of word cooccurrences [32].…”

Section: Discussionmentioning

confidence: 99%

Topic Representation of Researchers' Interests in a Large-Scale Academic Database and Its Application to Author Disambiguation

Katsurai

Ohmukai

Takeda

2016

IEICE Trans. Inf. & Syst.

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It is crucial to promote interdisciplinary research and recommend collaborators from different research fields via academic database analysis. This paper addresses a problem to characterize researchers' interests with a set of diverse research topics found in a large-scale academic database. Specifically, we first use latent Dirichlet allocation to extract topics as distributions over words from a training dataset. Then, we convert the textual features of a researcher's publications to topic vectors, and calculate the centroid of these vectors to summarize the researcher's interest as a single vector. In experiments conducted on CiNii Articles, which is the largest academic database in Japan, we show that the extracted topics reflect the diversity of the research fields in the database. The experiment results also indicate the applicability of the proposed topic representation to the author disambiguation problem.

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Section: Discussionmentioning

confidence: 99%

Topic Representation of Researchers' Interests in a Large-Scale Academic Database and Its Application to Author Disambiguation

Katsurai

Ohmukai

Takeda

2016

IEICE Trans. Inf. & Syst.

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“…[65], [21], [66], [67], [68], [69] [7], [175], [106], [8], [176], [177], [178], [179], [180], [181], [182], [57], [116] [187] , [190], [191], [75], [192], [31], [76], [193], [194] 5…”

Section: Model Based Techniquesunclassified

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

Sohail

Siddiqui

Ali

2017

JESTR

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This paper presents the state of art techniques in recommender systems (RS). The various techniques are diagrammatically illustrated which on one hand helps a naïve researcher in this field to accommodate the on-going researches and establish a strong base, on the other hand it focuses on different categories of the recommender systems with deep technical discussions. The review studies on RS are highlighted which helps in understanding the previous review works and their directions. 8 different main categories of recommender techniques and 19 sub categories have been identified and stated. Further, soft computing approach for recommendation is emphasized which have not been well studied earlier. The major problems of the existing area is reviewed and presented from different perspectives. However, solutions to these issues are rarely discussed in the previous works, in this study future direction for possible solutions are also addressed.

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“…Hybrid filtering is usually based on bio-inspired or probabilistic methods such as genetic algorithms (Ho, Fong, & Yan, 2007;Gao & Li, 2008), fuzzy genetic (Al-Shamri & Bharadwaj, 2008), neural networks (Lee, Choi, & Woo, 2002;Christakou& Stafylopatis, 2005;Ren, He, Gu, Xia, & Wu, 2008), bayesian networks (Campos, Fernández-Luna, Huete, & Rueda-Morales, 2010), clustering (Shinde & Kulkami, 2012) and latent features (Maneeroj & Takasu, 2009).…”

Section: Content-based Filteringmentioning

confidence: 99%

“…Weng, Xu, Li, and Nayak (2008) combine the implicit relations between users' items preferences and the additional taxonomic preferences to make better quality recommendations as well as alleviate the cold-start problem. Loh, Lorenzi, Granada, Lichtnow, Wives, and Oliveira Maneeroj and Takasu (2009) propose a hybrid RS that utilizes latent features extracted from items represented by a multi-attributed record using a probabilistic model. Park, Pennock, Madani, Good, and Coste (2006) propose a new approach: they use filterbots, and surrogate users that rate items based only on user or item attributes.…”

Section: The Cold Start Problemmentioning

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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Hybrid Recommender System Using Latent Features

Cited by 16 publications

References 8 publications

Topic Representation of Researchers' Interests in a Large-Scale Academic Database and Its Application to Author Disambiguation

Topic Representation of Researchers' Interests in a Large-Scale Academic Database and Its Application to Author Disambiguation

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

Incorporating group recommendations to recommender systems: Alternatives and performance

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