Existing multimodal recommenders largely rely on late fusion, where modalities are separately encoded and aligned before fusion. Such designs work well with complete content but become fragile in practice: modalities are often missing, alignment breaks down, and imputation adds complexity with limited benefit. We propose EFMRec, a missing-aware early-fusion framework that follows a relaxed principle of leveraging modalities when available and ignoring them when absent. By exploiting pretrained multimodal models, EFMRec projects available modalities into a shared semantic space and aggregates them through a missing-aware early fusion, producing unified representations without reconstruction or auxiliary losses. These representations are further propagated via a GCN-based architecture and integrated with a collaborative filtering backbone to jointly model multimodal and collaborative signals. Experiments on three benchmarks demonstrate that EFMRec consistently outperforms strong baselines under both full- and missing-modality settings, highlighting its robustness to modality incompleteness.

This paper is concerned with the one class classification(OCC) problem. By introducing the vector-valued function with regularizations in Y-valued Reproducing Hilbert Kernel Space(RHKS), we build an unsupervised classifier and discover the outliers and inliers simultaneously. Manifold regularization is employed to preserve the local similarity of data in input space. Experimental results of the proposed and comparing methods on OCC data sets demonstrate the performance of the proposed algorithm.

OBJECTIVE: To develop a predictive model for real-time predictions of length of stay, mortality, and readmission for hospitalized patients using electronic health records (EHRs). MATERIALS AND METHODS: A Bayesian Network model was built to estimate the probability of a hospitalized patient being "at home," in the hospital, or dead for each of the next 7 days. The network utilizes patient-specific administrative and laboratory data and is updated each time a new pathology test result becomes available. Electronic health records from 32 634 patients admitted to a Sydney metropolitan hospital via the emergency department from July 2008 through December 2011 were used. The model was tested on 2011 data and trained on the data of earlier years. RESULTS: The model achieved an average daily accuracy of 80% and area under the receiving operating characteristic curve (AUROC) of 0.82. The model's predictive ability was highest within 24 hours from prediction (AUROC = 0.83) and decreased slightly with time. Death was the most predictable outcome with a daily average accuracy of 93% and AUROC of 0.84. DISCUSSION: We developed the first non-disease-specific model that simultaneously predicts remaining days of hospitalization, death, and readmission as part of the same outcome. By providing a future daily probability for each outcome class, we enable the visualization of future patient trajectories. Among these, it is possible to identify trajectories indicating expected discharge, expected continuing hospitalization, expected death, and possible readmission. CONCLUSIONS: Bayesian Networks can model EHRs to provide real-time forecasts for patient outcomes, which provide richer information than traditional independent point predictions of length of stay, death, or readmission, and can thus better support decision making.

User preferences for products are constantly drifting over time as product perception and popularity are changing when new fashions or products emerge. Therefore, the ability to model the tendency of both user preferences and product attractiveness is vital to the design of recommender systems (RSs). However, conventional methods in RSs are incapable of modeling such a tendency accordingly, leading to unsatisfactory recommendation performance in many real-world deployments. In this paper, we develop a novel probabilistic temporal bilinear model for RSs, exploiting both temporal properties and dynamic information in user preferences and item attractiveness derived from the users' feedback over items, to simultaneously track latent factors that represent user preferences and item attractiveness. A learning and inference algorithm combining a sequential Monte Carlo method and the EM algorithm for this model is also developed to tackle the top-k recommendation problem over time. The proposed model is evaluated on three benchmark datasets. The experimental results demonstrate that our proposed model significantly outperforms a variety of existing methods for top-k recommendation.

This paper reports on the successful deployment of a peopleto-people recommender system in a large commercial online dating site. The deployment was the result of thorough evaluation and an online trial of a number of methods, including profile-based, collaborative filtering and hybrid algorithms. Results taken a few months after deployment show that key metrics generally hold their value or show an increase compared to the trial results, and that the recommender system delivered its projected benefits.

In this paper, we present a novel solution to the problem of human appearance matching across multiple cameras. Humans are represented by a set of feature points sampled from upper bodies. The problem of appearance matching across multiple cameras is formulated as finding corresponding points in two upper bodies from different views based on dissimilarity of region signatures as well as geometric constraints between feature points. For dissimilarity of region signatures, we first use k-means clustering to describe the region around the feature point, then estimate the dissimilarity between different regions under integer optimization framework. For geometric constraints, we get the spatial information of feature points based on a scale and rotation invariant constraint method. Lastly, agglomerative clustering algorithm is used to find the correct cluster of candidate pairs. Our method is robust to both outliers and deformation, and the experimental results show promising matching results on multiple cameras.

This paper shows how to improve the recommendations of an interaction-based collaborative filtering (IBCF) recommender used in online dating. Previous work has shown that IBCF works well in this domain, although it tends to rank popular candidates highly, which leads to these users receiving a large number of contacts. We address this problem by using a Decision Tree model as a “critic” to re-rank the candidates generated by IBCF, effectively promoting less popular candidates. This method was first evaluated on historical data from a large online dating site and then trialled live on the same site by providing recommendations to a large number of users throughout a 9 week period. The live trial confirmed the consistency of the analysis on historical data and the ability of the method to generate suitable candidates over an extended period. Our recommendations gave higher success rates than those for a control group made with a baseline recommender.

Predicting people who other people may like has recently become an important task in many online social networks. Traditional collaborative filtering (CF) approaches are popular in recommender systems to effectively predict user preferences for items. One major problem in CF is computing similarity between users or items. Traditional CF methods often use heuristic methods to combine the ratings given to an item by similar users, which may not reflect the characteristics of the active user and can give unsatisfactory performance. In contrast to heuristic approaches we have developed CollabNet, a novel algorithm that uses gradient descent to learn the relative contributions of similar users or items to the ranking of recommendations produced by a recommender system, using weights to represent the contributions of similar users for each active user. We have applied CollabNet to the challenging problem of people to people recommendation in social networks, where people have a dual role as both "users" and "items", e.g., both initiating and receiving communications, to recommend other users to a given user, based on user similarity in terms of both taste (whom they like) and attractiveness (who likes them). Evaluation of CollabNet recommendations on datasets from a commercial online social network shows improved performance over standard CF.

Robust visual tracking has become an important topic of research in computer vision. A novel method for robust object tracking, GATE [11], improves object tracking in complex environments using the particle filtering and the level set-based active contour method. GATE creates a spatial prior in the state space using shape information of the tracked object to filter particles in the state space in order to reshape and refine the posterior distribution of the particle filtering. This paper describes a comparative experiment that applies GATE and the standard particle filtering to track the object of interest in complex environments using simple features. Image sequences captured by the hand held, stationary and the PTZ camera are utilised. The experimental results demonstrate that GATE is able to solve the ambiguous outlier problem of particle filters in order to deal with heavy clutters in the background, occlusion, low resolution and noisy images, and thus significantly improves the particle filtering in object tracking.