Probabilistic latent preference analysis for collaborative filtering

Liu, Nathan N.; Zhao, Min; Yang, Qiang

doi:10.1145/1645953.1646050

Cited by 91 publications

(78 citation statements)

References 22 publications

(19 reference statements)

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“…CF techniques are divided into two categories: memorybased [23,4] and model-based [17,9]. Memory-based algorithms are heuristics to recommend by aggregating the preference of similar users.…”

Section: Related Workmentioning

confidence: 99%

Social recommendation across multiple relational domains

Jiang

Cui

Wang³

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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Social networks enable users to create different types of personal items. In dealing with serious information overload, the major problems of social recommendation are sparsity and cold start. In existing approaches, relational and heterogeneous domains can not be effectively utilized for social recommendation, which brings a challenge to model users and multiple types of items together on social networks. In this paper, we consider how to represent social networks with multiple relational domains and alleviate the major problems in an individual domain by transferring knowledge from other domains. We propose a novel Hybrid Random Walk (HRW), which can integrate multiple heterogeneous domains including directed/undirected links, signed/unsigned links and within-domain/cross-domain links into a star-structured hybrid graph with user graph at the center. We perform random walk until convergence and use the steady state distribution for recommendation. We conduct experiments on a real social network dataset and show that our method can significantly outperform existing social recommendation approaches.

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Section: Related Workmentioning

confidence: 99%

Social recommendation across multiple relational domains

Jiang

Cui

Wang³

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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“…[29] also uses a nuclear norm regularization, but that work assumes access to the true underlying ratings, while we assume access only to pairwise preferences. Other algorithms aggregate users ratings by exploiting the similarity of users by nearest neighbor search [5,28], low-rank matrix factorization [22,23,29], or probabilistic latent model [10,16]. However, as noted, numeric ratings can be highly varied even when preferences are shared.…”

Section: Introductionmentioning

confidence: 99%

Individualized rank aggregation using nuclear norm regularization

Negahban

2015

2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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In recent years rank aggregation has received significant attention from the machine learning community. The goal of such a problem is to combine the (partially revealed) preferences over objects of a large population into a single, relatively consistent ordering of those objects. However, in many cases, we might not want a single ranking and instead opt for individual rankings. We study a version of the problem known as collaborative ranking. In this problem we assume that individual users provide us with pairwise preferences (for example purchasing one item over another). From those preferences we wish to obtain rankings on items that the users have not had an opportunity to explore. The results here have a very interesting connection to the standard matrix completion problem. We provide a theoretical justification for a nuclear norm regularized optimization procedure, and provide high-dimensional scaling results that show how the error in estimating user preferences behaves as the number of observations increase.

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“…However to get a complete picture the method should also be compared to other latent factor pairwise preferences elicitation methods. [1], [9], [12].…”

Section: Discussionmentioning

confidence: 99%

Initial Profile Generation in Recommender Systems Using Pairwise Comparison

Rokach

Kisilevich

2012

IEEE Trans. Syst., Man, Cybern. C

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Abstract-Most recommender systems, such as collaborative filtering, cannot provide personalized recommendations until a user profile has been created. This is known as the new user cold-start problem. Several systems try to learn the new users' profiles as part of the sign up process by asking them to provide feedback regarding several items. We present a new, anytime preferences elicitation method that uses the idea of pairwise comparison between items. Our method uses a lazy decision tree, with pairwise comparisons at the decision nodes. Based on the user's response to a certain comparison, we select on-the-fly what pairwise comparison should next be asked. A comparative field study has been conducted for examining the suitability of the proposed method for eliciting the user's initial profile. The results indicate that the proposed pairwise approach provides more accurate recommendations than existing methods and requires less effort when signing up newcomers.

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Probabilistic latent preference analysis for collaborative filtering

Cited by 91 publications

References 22 publications

Social recommendation across multiple relational domains

Social recommendation across multiple relational domains

Individualized rank aggregation using nuclear norm regularization

Initial Profile Generation in Recommender Systems Using Pairwise Comparison

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