Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations

Lee, Seok Kee; Cho, Yoon Ho; Kim, Soung Hie

doi:10.1016/j.ins.2010.02.004

Cited by 204 publications

(82 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, preferences are based on beliefs and other characteristics of the investor. In [47], authors deal with similar characteristics. It this case, the compassion of SINVLIO and [47] produce comparable results (0.32 vs. 0.311).…”

Section: Discussionmentioning

confidence: 99%

SINVLIO: Using semantics and fuzzy logic to provide individual investment portfolio recommendations

García-Crespo

López-Cuadrado

González‐Carrasco

et al. 2012

Knowledge-Based Systems

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

SINVLIO: Using semantics and fuzzy logic to provide individual investment portfolio recommendations

García-Crespo

López-Cuadrado

González‐Carrasco

et al. 2012

Knowledge-Based Systems

View full text Add to dashboard Cite

“…Jullien-Ramasso et al (2012) presented a decision support system for animated films for the International Animated Film Festival organized at Annecy, France, with selection based on the multi-criteria aggregation of referees' ordinal preferences. Lee et al (2010) proposed collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations. Cook (2006) and Rebai et al (2006) also conducted relevant studies.…”

Section: Introductionmentioning

confidence: 99%

Lattice Order Group Decision Making with Interval Probability Based on Prospect Theory

Guo

Peng

2014

Group Decis Negot

View full text Add to dashboard Cite

A random lattice order decision analysis method is proposed based on an interval probability distribution preference vector by way of entropy theory, focusing on a decision preference system in which preference relation probability is described by interval values and the decision maker's behavior is also considered. The preference characterization of decision makers is extended from four varieties of relations to seven varieties of preference relations. In addition to the concept, property, and operation rules of interval probability, the concept of interval-valued distribution preference vectors and the relative entropy on the lattice-ordered preference system are given. Then, the interval probability can be more precisely determined, and the weighting interval probability is transformed into the interval probability weight. The ER nonlinear optimization model based on preference entropy is established, individual preferences are aggregated by applying the priority rule and the intersection rule, and the specific steps of decision making are given. Finally, the feasibility and effectiveness of the approach proposed in this paper are illustrated with a numerical example.

show abstract

“…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

View full text Add to dashboard Cite

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,

show abstract

Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations

Cited by 204 publications

References 48 publications

SINVLIO: Using semantics and fuzzy logic to provide individual investment portfolio recommendations

SINVLIO: Using semantics and fuzzy logic to provide individual investment portfolio recommendations

Lattice Order Group Decision Making with Interval Probability Based on Prospect Theory

Incorporating group recommendations to recommender systems: Alternatives and performance

Contact Info

Product

Resources

About