MovieLens Unplugged: Experiences with a Recommender System on Four Mobile Devices

Miller, Bradley N.; Albert, István; Lam, Shyong K.; Konstan, Joseph A.; Riedl, John

doi:10.1007/978-1-4471-3754-2_16

Cited by 162 publications

(183 citation statements)

References 8 publications

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“…They are integrated either in shopping websites, e.g., amazon.com or dedicated recommendation websites (Resnick et al 1994;Burke 2000;Miller et al 2003;Stolze and Ströbel 2003). The interaction models employed to acquire preferences vary from implicit to explicit methods.…”

Section: Methods Used In Recommender Systemsmentioning

confidence: 99%

“…To recommend items the attributes of the item are compared to the user's profile to see which items would be of interest to the user. Systems using collaborative filtering are, for instance, MovieLens (Miller et al 2003), or GroupLens (Resnick et al 1994) (www.grouplens.org), but also commercial systems like Amazon.com; an example of a system using a content-based method is the book recommender system developed by Mooney and Roy (2000).…”

Section: Methods Used In Recommender Systemsmentioning

confidence: 99%

See 1 more Smart Citation

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: Methods Used In Recommender Systemsmentioning

confidence: 99%

Section: Methods Used In Recommender Systemsmentioning

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

“…The interest in this area rapidly increasing because it constitute a problem rich research area and many of practical applications that support users to deal with data overload and supply personalized recommendations, services and content to them. Following are some examples of such applications-CDs , recommendation books, and news at VERSIFI Technologies (formerly AdaptiveInfo.com) [2], movies by MovieLens [3], products at Amazon [4]. Many vendors have preferred to incorporate recommendation capabilities into their commerce server.Recommender system also useful for calculating proper contents to smart TV.…”

Section: Introductionmentioning

confidence: 99%

“…Recommender systems can be generally divided into three types: collaborative filtering [5], content-based filtering [6] and hybrid approach, which uses both of the two methods [7][8] [9]. Collaborative filtering recommends contents by analyzing the common patterns of multiple users who contribute to the same interests [10][11] [12].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Personalized Multimedia Content Search

Vanashri¹

2015

IJRITCC

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Abstract-Millions of users share opinions on different aspects of life every day. User can give opinions through their comments related to multimedia contents. For analyzing user's demands we can evaluate user comments as a common online resource. Comments given b y user provide clues about them & also their interest area. In this paper, we done survey on different techniques used for personalized search in relevant information retrieval. Recommender systems or recommendation systems are a subclass of information filtering system that seek to predict 'rating' or 'preference' that are given by users. We propose a personalized search system, called MovieMine, enhance this proposed system to provide more relevant data summarized according to past & present comments left by user. Large movie data center provide movie review which can be utilized for more specific output. Experimental evaluations show that our proposed techniques are efficient and perform better than previously proposed methods. As in research work we can study more types of sentiment analysis with different recommender systems approach.

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“…This advantage of recommenders is even more valuable in small devices, on which the amount of screen real estate is minuscule, and through which users want immediately valuable information without having to browse through a large number of alternatives. Miller et al (2003) examined four interfaces to recommendations that could be used on mobile devices: (1) WAP, a Web technology for cell phone screens; (2) Avant-Go, a technology for synchronizing selected Web pages to a PDA; (3) live HTML on a PDA; and (4) a VoiceXML interface for speaking to the recommender over a telephone. The overall results were that users very much liked the portable recommenders, because they could get recommendations right when and where they needed them.…”

Section: Portable Recommendersmentioning

confidence: 99%