A Context-Aware Movie Preference Model Using a Bayesian Network for Recommendation and Promotion

Ono, Chihiro; Kurokawa, Mori; Motomura, Yoichi; Asoh, Hideki

doi:10.1007/978-3-540-73078-1_28

Cited by 59 publications

(40 citation statements)

References 14 publications

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“…Affect is also underexplored in current preference elicitation methods. Only the movie recommenders by Ono et al (2007) and whattorent.com ask for input about emotions or moods. However, underlying interests and affect are important aspects of human preference construction that should be considered in a preference elicitation interface.…”

Section: Support Of Human Preference Construction In Current Methodsmentioning

confidence: 99%

Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process

Pommeranz

Broekens

Wiggers

et al. 2012

User Model User-Adap Inter

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Two problems may arise when an intelligent (recommender) system elicits users' preferences. First, there may be a mismatch between the quantitative preference representations in most preference models and the users' mental preference models. Giving exact numbers, e.g., such as "I like 30 days of vacation 2.5 times better than 28 days" is difficult for people. Second, the elicitation process can greatly influence the acquired model (e.g., people may prefer different options based on whether a choice is represented as a loss or gain). We explored these issues in three studies. In the first experiment we presented users with different preference elicitation methods and found that cognitively less demanding methods were perceived low in effort and high in liking. However, for methods enabling users to be more expressive, the perceived effort was not an indicator of how much the methods were liked. We thus hypothesized that users are willing to spend more effort if the feedback mechanism enables them to be more expressive. We examined this hypothesis in two follow-up studies. In the second experiment, we explored the trade-off between giving detailed preference feedback and effort. We found that familiarity with and opinion about an item are important factors mediating this trade-off. Additionally, affective feedback was preferred over a finer grained one-dimensional rating scale for giving additional detail. In the third study, we explored the influence of the interface on the elicitation process in a participatory set-up. People considered it helpful to be able to explore the link between their interests, preferences and the desirability of outcomes. We also confirmed that people do not want to spend additional effort in cases where it seemed unnecessary. Based on the findings, we propose four design guidelines to foster interface design of preference elicitation from a user view.

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Section: Support Of Human Preference Construction In Current Methodsmentioning

confidence: 99%

Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process

Pommeranz

Broekens

Wiggers

et al. 2012

User Model User-Adap Inter

View full text Add to dashboard Cite

show abstract

“…Bayesian network model, which is also called probabilistic directed acyclic graphical model, operates with conditional probability. In the network, probability of a variable influencing others is evaluated and then recommendation will be given based on dependencies between pairs of variables [11], [19], [20].…”

Section: Collaborative Filteringmentioning

confidence: 99%

Effective Matrix Factorization for Online Rating Prediction

Zhou

Wong

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Recommender systems have been widely utilized by online merchants and online advertisers to promote their products in order to improve profits. By evaluating customer interests based on their purchase history and relating it to commodities for sale these retailers could excavate out products which are most likely to be chosen by a specific customer. In this case, online ratings given by customers are of great interest as they could reflect different levels of customers' interest on different products. Collaborative Filtering (CF) approach is chosen by a large amount of web-based retailers for their recommender systems because CF operates on interactions between customers and products. In this paper, a major approach of CF, Matrix Factorization, is modified to give more accurate recommendations by predicting online ratings.

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“…Recently, a Bayesian network [10] has drawn researchers' attention as a technique for user modeling [11]- [13]. A Bayesian network models uncertain phenomena, such as preferences and situations.…”

Section: Related Workmentioning

confidence: 99%

User-Adapted Recommendation of Content on Mobile Devices Using Bayesian Networks

Iwasaki

Mizuno

Hara

et al. 2010

IEICE Trans. Inf. & Syst.

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SUMMARYMobile devices, such as cellular phones and car navigation systems, are essential to daily life. People acquire necessary information and preferred content over communication networks anywhere, anytime. However, usability issues arise from the simplicity of user interfaces themselves. Thus, a recommendation of content that is adapted to a user's preference and situation will help the user select content. In this paper, we describe a method to realize such a system using Bayesian networks. This user-adapted mobile system is based on a user model that provides recommendation of content (i.e., restaurants, shops, and music that are suitable to the user and situation) and that learns incrementally based on accumulated usage history data. However, sufficient samples are not always guaranteed, since a user model would require combined dependency among users, situations, and contents. Therefore, we propose the LK method for modeling, which complements incomplete and insufficient samples using knowledge data, and CPT incremental learning for adaptation based on a small number of samples. In order to evaluate the methods proposed, we applied them to restaurant recommendations made on car navigation systems. The evaluation results confirmed that our model based on the LK method can be expected to provide better generalization performance than that of the conventional method. Furthermore, our system would require much less operation than current car navigation systems from the beginning of use. Our evaluation results also indicate that learning a user's individual preference through CPT incremental learning would be beneficial to many users, even with only a few samples. As a result, we have developed the technology of a system that becomes more adapted to a user the more it is used.

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A Context-Aware Movie Preference Model Using a Bayesian Network for Recommendation and Promotion

Cited by 59 publications

References 14 publications

Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process

Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process

Effective Matrix Factorization for Online Rating Prediction

User-Adapted Recommendation of Content on Mobile Devices Using Bayesian Networks

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