Results 11 - 20
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171
A Generalized Linear Model for Principal Component Analysis of Binary Data
- In Proceedings of the 9 th International Workshop on Artificial Intelligence and Statistics
, 2003
"... We investigate a generalized linear model for dimensionality reduction of binary data. The model is related to principal component analysis (PCA) in the same way that logistic regression is related to linear regression. Thus we refer to the model as logistic PCA. In this paper, we derive an al ..."
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Cited by 59 (2 self)
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We investigate a generalized linear model for dimensionality reduction of binary data. The model is related to principal component analysis (PCA) in the same way that logistic regression is related to linear regression. Thus we refer to the model as logistic PCA. In this paper, we derive an alternating least squares method to estimate the basis vectors and generalized linear coe#cients of the logistic PCA model. The resulting updates have a simple closed form and are guaranteed at each iteration to improve the model's likelihood. We evaluate the performance of logistic PCA---as measured by reconstruction error rates---on data sets drawn from four real world applications.
Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models
- WWW 2009 MADRID! TRACK: SOCIAL NETWORKS AND WEB 2.0 / SESSION: RECOMMENDER SYSTEMS
, 2009
"... In Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendations for new users. We propose a feature-based machine learning approach to personalized recommendation that is capable o ..."
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Cited by 54 (3 self)
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In Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendations for new users. We propose a feature-based machine learning approach to personalized recommendation that is capable of handling the cold-start issue effectively. We maintain profiles of content of interest, in which temporal characteristics of the content, e.g. popularity and freshness, are updated in real-time manner. We also maintain profiles of users including demographic information and a summary of user activities within Yahoo! properties. Based on all features in user and content profiles, we develop predictive bilinear regression models to provide accurate personalized recommendations of new items for both existing and new users. This approach results in an offline model with light computational overhead compared with other recommender systems that require online re-training. The proposed framework is general and flexible for other personalized tasks. The superior performance of our approach is verified on a large-scale data set collected from the Today-Module on Yahoo! Front Page, with comparison against six competitive approaches.
PLDA: Parallel Latent Dirichlet Allocation for Large-scale Applications
"... Abstract. This paper presents PLDA, our parallel implementation of Latent Dirichlet Allocation on MPI and MapReduce. PLDA smooths out storage and computation bottlenecks and provides fault recovery for lengthy distributed computations. We show that PLDA can be applied to large, real-world applicatio ..."
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Cited by 47 (5 self)
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Abstract. This paper presents PLDA, our parallel implementation of Latent Dirichlet Allocation on MPI and MapReduce. PLDA smooths out storage and computation bottlenecks and provides fault recovery for lengthy distributed computations. We show that PLDA can be applied to large, real-world applications and achieves good scalability. We have released MPI-PLDA to open source at http://code.google.com/p/plda under the Apache License. 1
REFEREE: An open framework for practical testing of recommender systems using ResearchIndex
, 2002
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A maximum entropy approach to collaborative filtering in dynamic, sparse, high-dimensional domains
, 2002
"... We develop a maximum entropy (maxent) approach to generating recommendations in the context of a user’s current navigation stream, suitable for environments where data is sparse, highdimensional, and dynamic—conditions typical of many recommendation applications. We address sparsity and dimensionali ..."
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Cited by 38 (6 self)
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We develop a maximum entropy (maxent) approach to generating recommendations in the context of a user’s current navigation stream, suitable for environments where data is sparse, highdimensional, and dynamic—conditions typical of many recommendation applications. We address sparsity and dimensionality reduction by first clustering items based on user access patterns so as to attempt to minimize the apriori probability that recommendations will cross cluster boundaries and then recommending only within clusters. We address the inherent dynamic nature of the problem by explicitly modeling the data as a time series; we show how this representational expressivity fits naturally into a maxent framework. We conduct experiments on data from ResearchIndex, a popular online repository of over 470,000 computer science documents. We show that our maxent formulation outperforms several competing algorithms in offline tests simulating the recommendation of documents to ResearchIndex users. 1
Global Grid Forum 5
- in 3rd International Conference on Trust Management (iTrust 2005
, 2005
"... Abstract. Collaborative Filtering (CF), the prevalent recommendation approach, has been successfully used to identify users that can be characterized as “similar ” according to their logged history of prior transactions. However, the applicability of CF is limited due to the sparsity problem, which ..."
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Cited by 34 (1 self)
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Abstract. Collaborative Filtering (CF), the prevalent recommendation approach, has been successfully used to identify users that can be characterized as “similar ” according to their logged history of prior transactions. However, the applicability of CF is limited due to the sparsity problem, which refers to a situation that transactional data are lacking or are insufficient. In an attempt to provide high-quality recommendations even when data are sparse, we propose a method for alleviating sparsity using trust inferences. Trust inferences are transitive associations between users in the context of an underlying social network and are valuable sources of additional information that help dealing with the sparsity and the cold-start problems. A trust computational model has been developed that permits to define the subjective notion of trust by applying confidence and uncertainty properties to network associations. We compare our method with the classic CF that does not consider any transitive associations. Our experimental results indicate that our method of trust inferences significantly improves the quality performance of the classic CF method. 1
S.: Combining eye movements and collaborative filtering for proactive information retrieval
- In: Proc. SIGIR
, 2005
"... We study a new task, proactive information retrieval by combining implicit relevance feedback and collaborative fil-tering. We have constructed a controlled experimental set-ting, a prototype application, in which the users try to find interesting scientific articles by browsing their titles. Im-pli ..."
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Cited by 34 (15 self)
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We study a new task, proactive information retrieval by combining implicit relevance feedback and collaborative fil-tering. We have constructed a controlled experimental set-ting, a prototype application, in which the users try to find interesting scientific articles by browsing their titles. Im-plicit feedback is inferred from eye movement signals, with discriminative hidden Markov models estimated from exist-ing data in which explicit relevance feedback is available. Collaborative filtering is carried out using the User Rating Profile model, a state-of-the-art probabilistic latent variable model, computed using Markov Chain Monte Carlo tech-niques. For new document titles the prediction accuracy with eye movements, collaborative filtering, and their com-bination was significantly better than by chance. The best
Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences
- Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR
, 2006
"... This paper presents a hybrid music recommendation method that solves problems of two prominent conventional methods: collaborative filtering and content-based recommendation. The former cannot recommend musical pieces that have no ratings because recommendations are based on actual user ratings. In ..."
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Cited by 31 (1 self)
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This paper presents a hybrid music recommendation method that solves problems of two prominent conventional methods: collaborative filtering and content-based recommendation. The former cannot recommend musical pieces that have no ratings because recommendations are based on actual user ratings. In addition, artist variety in recommended pieces tends to be poor. The latter, which recommends musical pieces that are similar to users ’ favorites in terms of music content, has not been fully investigated. This induces unreliability in modeling of user preferences; the content similarity does not completely reflect the preferences. Our method integrates both rating and content data by using a Bayesian network called an aspect model. Unobservable user preferences are directly represented by introducing latent variables, which are statistically estimated. To verify our method, we conducted experiments by using actual audio signals of Japanese songs and the corresponding rating data collected from Amazon. The results showed that our method outperforms the two conventional methods in terms of recommendation accuracy and artist variety and can reasonably recommend pieces even if they have no ratings.
An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model
"... Abstract—This paper presents a hybrid music recommender system that ranks musical pieces while efficiently maintaining collaborative and content-based data, i.e., rating scores given by users and acoustic features of audio signals. This hybrid approach overcomes the conventional tradeoff between rec ..."
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Cited by 30 (3 self)
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Abstract—This paper presents a hybrid music recommender system that ranks musical pieces while efficiently maintaining collaborative and content-based data, i.e., rating scores given by users and acoustic features of audio signals. This hybrid approach overcomes the conventional tradeoff between recommendation accuracy and variety of recommended artists. Collaborative filtering, which is used on e-commerce sites, cannot recommend nonbrated pieces and provides a narrow variety of artists. Content-based filtering does not have satisfactory accuracy because it is based on the heuristics that the user’s favorite pieces will have similar musical content despite there being exceptions. To attain a higher recommendation accuracy along with a wider variety of artists, we use a probabilistic generative model that unifies the collaborative and content-based data in a principled way. This model can explain the generative mechanism of the observed data in the probability theory. The probability distribution over users, pieces, and features is decomposed into three conditionally independent ones by introducing latent variables. This decomposition enables us to efficiently and incrementally adapt the model for increasing numbers of users and rating scores. We evaluated our system by using audio signals of commercial CDs and their corresponding rating scores obtained from an e-commerce site. The results revealed that our system accurately recommended pieces including nonrated ones from a wide variety of artists and maintained a high degree of accuracy even when new users and rating scores were added. Index Terms—Aspect model, hybrid collaborative and contentbased recommendation, incremental training, music recommender system, probabilistic generative model. I.
