Using Groups of Items for Preference Elicitation in Recommender Systems

Chang, Shuo; Harper, F. Maxwell; Terveen, Loren

doi:10.1145/2675133.2675210

Cited by 42 publications

(47 citation statements)

References 32 publications

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“…This algorithm was called the Peasant, and was described as "non-personalized".  The Pick-Groups recommender is an item-item collaborative filter that uses synthetic item ratings derived from the user's choice of different movie groups [3]. It is intended to provide an improved user experience for new users of the system.…”

Section: Figure 1: Recommender Switching Controlmentioning

confidence: 99%

Letting Users Choose Recommender Algorithms

Ekstrand

Kluver

Harper

et al. 2015

Proceedings of the 9th ACM Conference on Recommender Systems

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Recommender systems are not one-size-fits-all; different algorithms and data sources have different strengths, making them a better or worse fit for different users and use cases. As one way of taking advantage of the relative merits of different algorithms, we gave users the ability to change the algorithm providing their movie recommendations and studied how they make use of this power. We conducted our study with the launch of a new version of the MovieLens movie recommender that supports multiple recommender algorithms and allows users to choose the algorithm they want to provide their recommendations. We examine log data from user interactions with this new feature to understand whether and how users switch among recommender algorithms, and select a final algorithm to use. We also look at the properties of the algorithms as they were experienced by users and examine their relationships to user behavior.We found that a substantial portion of our user base (25%) used the recommender-switching feature. The majority of users who used the control only switched algorithms a few times, trying a few out and settling down on an algorithm that they would leave alone. The largest number of users prefer a matrix factorization algorithm, followed closely by item-item collaborative filtering; users selected both of these algorithms much more often than they chose a non-personalized mean recommender. The algorithms did produce measurably different recommender lists for the users in the study, but these differences were not directly predictive of user choice.

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Section: Figure 1: Recommender Switching Controlmentioning

confidence: 99%

Letting Users Choose Recommender Algorithms

Ekstrand

Kluver

Harper

et al. 2015

Proceedings of the 9th ACM Conference on Recommender Systems

Self Cite

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“…Although an explicit rating approach increases the user effort, it is commonly used because it respects the user integrity, it allows for a greater transparency, and it is more reliable than implicit data in many cases. Moreover, according to Chang et al, 10 users who receive poor initial recommendations are less active than other users. However, this is not an efficient way to convert the workload carried by the user in recommendations as it involves a high user's cognitive effort.…”

Section: User Profilingmentioning

confidence: 99%

“…15 One of the most common approach used in RSs to generate a user preference profile relies to the new user to express his or her preferences by rating a fixed number of items. While many approaches deal with the problem of providing algorithmic solutions to maximize the information obtained from the user ratings, 9,16 other approaches are starting to rethink the initial elicitation process as the previous studies, 10,11 avoiding the user to directly rate individual items, but expressing their opinion on groups of items, or only selecting relevant items without providing explicit ratings. Typically, an elicitation process that is too slow or complex can lead the user to insert incorrect or incomplete information, 15 or, eventually, not to use the RS.…”

Section: User Profilingmentioning

confidence: 99%

“…10 On this basis, the following approach requires the user to provide his or her preferences directly on groups of similar items as in Chang et al, 10 thus, gaining in efficiency with respect to the cost of losing information on individual items ratings. 10 On this basis, the following approach requires the user to provide his or her preferences directly on groups of similar items as in Chang et al, 10 thus, gaining in efficiency with respect to the cost of losing information on individual items ratings.…”

Section: A Categories Clustering Approachmentioning

confidence: 99%

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A comparison of two preference elicitation approaches for museum recommendations

Rossi

Barile

Martino

et al. 2017

Concurrency and Computation

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Recommendation systems based on collaborative filtering methods can be exploited in the context of providing personalized artworks tours within a museum. However, to be effectively used, we have several problems to be addressed: user preferences are not expressed as rating and recommendation systems must provide for new users efficient and simple preferences elicitation processes that do not require much effort and time. In this work, we present and evaluate 2 state-of-the-art approaches that share the aim not to rely on individual item ratings. The first method uses a clustering algorithm to categorize items and provide recommendations, while the second one is inspired by the matrix factorization approach to select a couples of item groups that users have to evaluate to obtain preference profiles. We evaluate the 2 approaches with both an off-line simulation and a user study with the aim to find the optimal configuration as well as to evaluate the effectiveness of the 2 proposed methods. Results show that the elicitation processes permit to obtain preference profiles in a time substantially less than the baseline method, while the differences in terms of prediction accuracy are minimal

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“…Then model based approaches including the greedy algorithm, item clustering, and boosting multiple tree [12] are been employed. In order to boost the elicitations, Chang et al [6] proposed to ask multiple questions at each trial with groups of items. However, to the best of our knowledge, little literature is available on representative rating sampling for the heavy users with vast ratings.…”

Section: Related Workmentioning

confidence: 99%

A Novel Framework to Process the Quantity and Quality of User Behavior Data in Recommender Systems

Lin

Yao

2016

Web-Age Information Management

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Abstract. Recommender system has become one of the most popular techniques to cope with the information overload problem. In the past years many algorithms have been proposed to obtain accurate recommendations. Such methods usually put all the collected user data into learning models without a careful consideration of the quantity and quality of individual user feedbacks. Yet in real applications, different types of users tend to represent preferences and opinions in various ways, thus resulting in user data with radically diverse quantity and quality. This characteristic of data influences the performance of recommendations. However, little attention has been devoted to the management of quantity and quality for user data in recommender systems. In this paper, we propose a generic framework to seamlessly exploit different pre-processing and recommendation approaches for ratings of different users. More specifically, we first classify users into groups based on the quantity and quality of their behavior data. In order to handle the user groups diversely, we further propose several data pre-processing strategies. Subsequently, we present a novel transfer latent factor model (TLMF) to transfer learnt models between groups. Finally, we conduct extensive experiments on a large data set and demonstrate the effectiveness of our proposed framework.

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Using Groups of Items for Preference Elicitation in Recommender Systems

Cited by 42 publications

References 32 publications

Letting Users Choose Recommender Algorithms

Letting Users Choose Recommender Algorithms

A comparison of two preference elicitation approaches for museum recommendations

A Novel Framework to Process the Quantity and Quality of User Behavior Data in Recommender Systems

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