Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
84
Probabilistic Collaborative Filtering with Negative Cross Entropy
"... Relevance-Based Language Models are an effective IR approach which explicitly introduces the concept of relevance in the sta-tistical Language Modelling framework of Information Retrieval. These models have shown to achieve state-of-the-art retrieval per-formance in the pseudo relevance feedback tas ..."
Abstract
- Add to MetaCart
task. In this paper we propose a novel adaptation of this language modeling approach to rating-based Collaborative Filtering. In a memory-based approach, we apply the model to the formation of user neighbourhoods, and the generation of recommendations based on such neighbourhoods. We report
1Probabilistic Collaborative Filtering with Negative Cross Entropy
"... • Neighbourhood identification in memory-based CF algorithms is based on selecting those users who are most similar to the active user according to a ..."
Abstract
- Add to MetaCart
• Neighbourhood identification in memory-based CF algorithms is based on selecting those users who are most similar to the active user according to a
Strictly Proper Scoring Rules, Prediction, and Estimation
, 2007
"... Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distribution F if he ..."
Abstract
-
Cited by 373 (28 self)
- Add to MetaCart
measures, entropy functions, and Bregman divergences. In the case of categorical variables, we prove a rigorous version of the Savage representation. Examples of scoring rules for probabilistic forecasts in the form of predictive densities include the logarithmic, spherical, pseudospherical, and quadratic
Cross-domain mediation in collaborative filtering
- User Modeling, volume 4511 of Lecture Notes in Computer Science
, 2007
"... Abstract. One of the main problems of collaborative filtering recommenders is the sparsity of the ratings in the users-items matrix, and its negative effect on the prediction accuracy. This paper addresses this issue applying cross-domain mediation of collaborative user models, i.e., importing and a ..."
Abstract
-
Cited by 26 (1 self)
- Add to MetaCart
Abstract. One of the main problems of collaborative filtering recommenders is the sparsity of the ratings in the users-items matrix, and its negative effect on the prediction accuracy. This paper addresses this issue applying cross-domain mediation of collaborative user models, i.e., importing
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 ..."
Abstract
-
Cited by 38 (6 self)
- Add to MetaCart
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
Probabilistic Factorization . . .
"... In this paper we present a probabilistic algorithm which factorizes non-negative data. We employ entropic priors to additionally satisfy that user specified pairs of factors in this model will have their cross entropy maximized or minimized. These priors allow us to construct factorization algorithm ..."
Abstract
- Add to MetaCart
In this paper we present a probabilistic algorithm which factorizes non-negative data. We employ entropic priors to additionally satisfy that user specified pairs of factors in this model will have their cross entropy maximized or minimized. These priors allow us to construct factorization
A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2011
"... Abstract—Increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level imag ..."
Abstract
-
Cited by 8 (0 self)
- Add to MetaCart
tag to tag co-occurrence probabilities. We developed a collaborative filtering method based on non-negative matrix factorization (NMF) for tackling this data sparsity issue. Also, an L1 norm kernel method is used to estimate the correlations between image features and semantic concepts
Cross-domain Collaborative Anomaly Detection: So Far Yet So Close ⋆
"... Abstract. Web applications have emerged as the primary means of access to vital and sensitive services such as online payment systems and databases storing personally identifiable information. Unfortunately, the need for ubiquitous and often anonymous access exposes web servers to adversaries. Indee ..."
Abstract
-
Cited by 5 (3 self)
- Add to MetaCart
that is deemed abnormal by local Content Anomaly Detection (CAD) sensors. The cross-site information exchange happens in real-time leveraging privacy preserving data structures. We filter out high entropy and rarely seen legitimate requests reducing the amount of data and time an operator has to spend sifting
A Novel Non-Negative Matrix Factorization Method for Recommender Systems
, 2015
"... Abstract: Recommender systems collect various kinds of data to create their recommendations. Collaborative filtering is a common technique in this area. This technique gathers and analyzes information on users preferences, and then estimates what users will like based on their similarity to other u ..."
Abstract
- Add to MetaCart
users. However, most of current collaborative filtering approaches have faced two problems: sparsity and scalability. This paper proposes a novel method by applying non-negative matrix factorization, which alleviates these problems via matrix factorization and similarity. Non-negative matrix
Respectful Cameras: Detecting Visual Markers in Real-Time to Address Privacy Concerns
"... Abstract To address privacy concerns regarding digital video surveillance cameras, we propose a practical, real-time approach that preserves the ability to observe actions while obscuring individual identities. In the Respectful Cameras system, people who wish to remain anonymous wear colored marker ..."
Abstract
-
Cited by 32 (3 self)
- Add to MetaCart
of the scene. Our approach uses a visual color-tracker based on a nine dimensional color-space using a Probabilistic Adaptive Boosting (AdaBoost) classifier with axis-aligned hyperplanes as weak hypotheses. We then use Sampling Importance Resampling (SIR) Particle Filtering to incorporate interframe temporal
Results 1 - 10
of
84
