TV Program Recommendation for Multiple Viewers Based on user Profile Merging

Yu, Zhiwen; Zhou, Xingshe; Hao, Yanbin; Gu, Jianhua

doi:10.1007/s11257-006-9005-6

Cited by 312 publications

(179 citation statements)

References 13 publications

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“…RS that consider groups of users (Jameson & Smyth, 2007;Boratto & Carta, 2011) are starting to expand and to be used in different areas: tourism (Ardissono, Goy, Petrone, Segnan, & Torasso, 2003;McCarthy, Salamó, Coyle, McGinty, Smyth, & Nixon, 2006), music (Chao, Balthrop, & Forrest, 2005), TV (Yu, Zhou, Hao, & Gu, 2006) and web (Pazzani & Billsus, 2007).…”

Section: Group Recommendationsmentioning

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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Section: Group Recommendationsmentioning

confidence: 99%

Incorporating group recommendations to recommender systems: Alternatives and performance

Ortega

Bobadilla

Hernando

et al. 2013

Information Processing & Management

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“…Group Recommending: Although the problem of group recommending is relatively new, the same dichotomy can be found, depending on whether they use individuals opinions to get to a consensus recommendations [9,10,11] or not [1,2,12]. In general, when focusing on "mathematical" approaches, ad hoc combinations criteria have been used.…”

Section: Combining Probability Distributionsmentioning

confidence: 99%

Average and Majority Gates: Combining Information by Means of Bayesian Networks

Campos

Fernández‐Luna

Huete

et al. 2007

Lecture Notes in Computer Science

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Abstract. In this paper we focus on the problem of belief aggregation, i.e. the task of forming a group consensus probability distribution by combining the beliefs of the individual members of the group. We propose the use of Bayesian Networks to model the interactions between the individuals of the group and introduce average and majority canonical models and their application to information aggregation. Due to efficiency restrictions imposed by the Group Recommending problem, where our research is framed, we have had to develop specific inference algorithms to compute group recommendations.

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“…Some early systems were developed in a variety of domains, such as, group web page recommendation (Lieberman et al 1999), tour packages for groups of tourists (Ardissono et al 2003), music tracks and playlists for large groups of many listeners (Crossen et al 2002), movies and TV programs for friends and family (O'Connor et al 2001;Yu et al 2006). Group scenarios are especially popular in the food domain in which a group of family members, friends or colleagues wants to make a party or simply have a meal together.…”

Section: Introductionmentioning

confidence: 99%

An overview of recommender systems in the healthy food domain

et al. 2017

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Recently, food recommender systems have received increasing attention due to their relevance for healthy living. Most existing studies on the food domain focus on recommendations that suggest proper food items for individual users on the basis of considering their preferences or health problems. These systems also provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Also, group recommendation functionalities are very useful in the food domain, especially when a group of users wants to have a dinner together at home or have a birthday party in a restaurant. Such scenarios create many challenges for food recommender systems since the preferences of all group members have to be taken into account in an adequate fashion. In this paper, we present an overview of recommendation techniques for individuals and groups in the healthy food domain. In addition, we analyze the existing state-of-the-art in food recommender systems and discuss research challenges related to the development of future food recommendation technologies.

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TV Program Recommendation for Multiple Viewers Based on user Profile Merging

Cited by 312 publications

References 13 publications

Incorporating group recommendations to recommender systems: Alternatives and performance

Incorporating group recommendations to recommender systems: Alternatives and performance

Average and Majority Gates: Combining Information by Means of Bayesian Networks

An overview of recommender systems in the healthy food domain

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