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In recommendation tasks, we model user preferences by learning node representations (i.e., user and item embeddings) based on the observed user-item interaction data, which is a bipartite graph. Graph Neural Networks (GNNs) are widely used to refine the representations by exploring the topology of the graph: embeddings of neighbors are propagated to each node to reconstruct its embeddings. However, the propagation strategy in existing GNNs is empirical and defective: (1) a substantial proportion of links are missed in the sparse observed graph, which causes ineffective and biased propagation; and (2) the propagation weights are determined by a coarse pre-defined rule, which only takes the degree of nodes into consideration. In this paper, we propose a dense and data-driven propagation mechanism for GNNs. Considering the graph we use to propagate embeddings in recommendation tasks is extremely sparse, we complement it and use the predicted graph as the new propagation tool. We learn the propagation matrix from the data, and propose a Self-propagation Graph Neural Network (SGNN). Since it is very space- and time-consuming to maintain a large and dense propagation matrix, we factorize it for storing and updating. In this paper, we propose three methods to complete the sparse graph and construct the propagation matrix: (1) we complete the graph based on a recommendation model; (2) we measure the node distance based on spectral clustering; (3) we predict missing links of the graph based on predictive embeddings. In SGNN, the embeddings can be propagated to not only the observed neighbors, but also the potential yet unobserved neighbors, and the propagation weights are learned based on the connection strength. Comprehensive experiments on three real-world datasets demonstrate the effectiveness and efficiency of our proposed model: SGNN outperforms recent state-of-the-art GNNs significantly. Codes are available on https://github.com/Wenhui-Yu/LCFN. [ABSTRACT FROM AUTHOR]

The implicit feedback based collaborative filtering (CF) has attracted much attention in recent years, mainly because users implicitly express their preferences in many real-world scenarios. The current mainstream pairwise methods optimize the Area Under the Curve (AUC) and are empirically proved to be helpful to exploit binary relevance data, but lead to either not address the ranking problem, or not specifically focus on top- $k$ k recommendation. Although there exists the listwise method maximizes the Mean Reciprocal Rank (MRR), it has low efficiency and is not particularly adequate for general implicit feedback situations. To that end, in this paper, we propose a new framework, namely Collaborative List-and-Pairwise Filtering (CLAPF), which aims to introduce pairwise thinking into listwise methods. Specifically, we smooth another well-known rank-biased measure called Mean Average Precision (MAP), and respectively combine two rank-biased metrics (MAP, MRR) with the pairwise objective function to capture the performance of top- $k$ k recommendation. Furthermore, the sampling scheme for CLAPF is discussed to accelerate the convergence speed. Our CLAPF framework is a new hybrid model that provides an idea of utilizing rank-biased measures in a pairwise way on implicit feedback. Empirical studies demonstrated CLAPF outperforms state-of-the-art approaches on real-world datasets. [ABSTRACT FROM AUTHOR]

Data in many real-world applications such as social networks, users shopping behaviors, and inter-item relationships can be represented as graphs. Graph Neural Networks (GNNs) have shown great success in learning meaningful representations for graphs by inherently integrating node information and topological structure. Data in social recommendations can also be denotes as graph data in the form of user-user social graphs and user-item graphs. In addition, the relationships between items can be denoted as item-item graphs. GNNs provide an unprecedented opportunity to advance social recommendations. However, there are tremendous challenges in building GNNs-based social recommendations where (1) users (items) are simultaneously involved in the user-item graph and user-user social graph (item-item graph); (2) user-item graphs not only contain user-item interactions but also include users’ opinions on items; and (3) the nature of social relations are heterogeneous among users. In this paper, we propose a novel graph neural network framework (GraphRec+) for social recommendations, which is able to coherently model graph data in order to learn better user and item representations. Specifically, we introduce a principled approach for jointly capturing interactions and opinions in the user-item graph and also propose an attention mechanism to differentiate the heterogeneous strengths of social relations. Comprehensive experiments on three real-world datasets show the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Because of strict response-time constraints, efficiency of top-k recommendation is crucial for real-world recommender systems. Locality sensitive hashing and index-based methods usually store both index data and item feature vectors in main memory, so they handle a limited number of items. Hashing-based recommendation methods enjoy low memory cost and fast retrieval of items, but suffer from large accuracy degradation. In this paper, we propose product Quantized Collaborative Filtering (pQCF) for better trade-off between efficiency and accuracy. pQCF decomposes a joint latent space of users and items into a Cartesian product of low-dimensional subspaces, and learns clustered representation within each subspace. A latent factor is then represented by a short code, which is composed of subspace cluster indexes. A user’s preference for an item can be efficiently calculated via table lookup. We then develop block coordinate descent for efficient optimization and reveal the learning of latent factors is seamlessly integrated with quantization. We further investigate an asymmetric pQCF, dubbed as QCF, where user latent factors are not quantized and shared across different subspaces. The extensive experiments with 6 real-world datasets show that pQCF significantly outperforms the state-of-the-art hashing-based CF and QCF increases recommendation accuracy compared to pQCF. [ABSTRACT FROM AUTHOR]

Most existing recommender systems leverage user behavior data of one type only, such as the purchase behavior in E-commerce that is directly related to the business Key Performance Indicator (KPI) of conversion rate. Besides the key behavioral data, we argue that other forms of user behaviors also provide valuable signal, such as views, clicks, adding a product to shopping carts and so on. They should be taken into account properly to provide quality recommendation for users. In this work, we contribute a new solution named short for Neural Multi-Task Recommendation (NMTR) for learning recommender systems from user multi-behavior data. We develop a neural network model to capture the complicated and multi-type interactions between users and items. In particular, our model accounts for the cascading relationship among different types of behaviors (e.g., a user must click on a product before purchasing it). To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task. Extensive experiments on two real-world datasets demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data. Further analysis shows that modeling multiple behaviors is particularly useful for providing recommendation for sparse users that have very few interactions. [ABSTRACT FROM AUTHOR]

Matrix approximation (MA) methods are integral parts of today's recommender systems. In standard MA methods, only one feature vector is learned for each user/item, which may not be accurate enough to characterize the diverse interests of users/items. For instance, users could have different opinions on a given item, so that they may need different feature vectors for the item to represent their unique interests. To this end, this article proposes a mixture matrix approximation (MMA) method, in which we assume that the user-item ratings follow mixture distributions and the user/item feature vectors vary among different stars to better characterize the diverse interests of users/items. Furthermore, we show that the proposed method can tackle both rating prediction and the top-N recommendation problems. Empirical studies on MovieLens, Netflix and Amazon datasets demonstrate that the proposed method can outperform state-of-the-art MA-based collaborative filtering methods in both rating prediction and top-N recommendation tasks. [ABSTRACT FROM AUTHOR]

Neural attention, an emerging technique used to identify important inputs within neural networks, have become increasingly popular in the area of recommender systems. Not only allowing to better identify what defines users and items, attention-based recommender systems are further able to provide accompanying explanations. However, these representations usually capture only part of users’ preferences and items’ attributes, resulting in limited reasoning and accuracy. We therefore propose Dual Attention Recommender with Items and Attributes (DARIA), a novel approach able to combine two dependable neural attention mechanisms to better justify its suggestions. Utilizing the personalized history of users, DARIA identifies the most relevant past activities while considering the real-world features that contributed to the similarity. In addition, we adopt the novel approach of self-attention and introduce Self-Attention Recommender based on Attributes and History (SARAH). As a variation to DARIA, SARAH utilizes two self-attention components to describe users by their most characteristic past activities and items by their best depicting attributes. Various experiments establish the significant improvement of SARAH and DARIA over seven key baselines in diverse recommendation settings. By comparing our two proposed frameworks, we demonstrate the potential benefit of applying self-attention in different scenarios. [ABSTRACT FROM AUTHOR]

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advances on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR. [ABSTRACT FROM AUTHOR]

Group recommendation has become highly demanded when users communicate in the forms of group activities in online sharing communities. These group activities include student group study, family TV program watching, friends travel decision, etc. Existing group recommendation techniques mainly focus on the small user groups. However, online sharing communities have enabled group activities among thousands of users. Accordingly, recommendation over large groups has become urgent. In this paper, we propose a new framework to accomplish this goal by exploring the group interests and the connections between group users. We first divide a big group into different interest subgroups, each of which contains users closely connected with each other and sharing the similar interests. Then, for each interest subgroup, our framework exploits the connections between group users to collect a comparably compact potential candidate set of media-user pairs, on which the collaborative filtering is performed to generate an interest subgroup-based recommendation list. After that, a novel aggregation function is proposed to integrate the recommended media lists of all interest subgroups as the final group recommendation results. Extensive experiments have been conducted on two real social media datasets to demonstrate the effectiveness and efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

Matrix and tensor completion aim to recover a low-rank matrix / tensor from limited observations and have been commonly used in applications such as recommender systems and multi-relational data mining. A state-of-the-art matrix completion algorithm is Soft-Impute, which exploits the special "sparse plus low-rank" structure of the matrix iterates to allow efficient SVD in each iteration. Though Soft-Impute is a proximal algorithm, it is generally believed that acceleration destroys the special structure and is thus not useful. In this paper, we show that Soft-Impute can indeed be accelerated without comprising this structure. To further reduce the iteration time complexity, we propose an approximate singular value thresholding scheme based on the power method. Theoretical analysis shows that the proposed algorithm still enjoys the fast $O(1/T^2)$O(1/T2) convergence rate of accelerated proximal algorithms. We also extend the proposed algorithm to tensor completion with the scaled latent nuclear norm regularizer. We show that a similar "sparse plus low-rank" structure also exists, leading to low iteration complexity and fast $O(1/T^2)$O(1/T2) convergence rate. Besides, the proposed algorithm can be further extended to nonconvex low-rank regularizers, which have better empirical performance than the convex nuclear norm regularizer. Extensive experiments demonstrate that the proposed algorithm is much faster than Soft-Impute and other state-of-the-art matrix and tensor completion algorithms. [ABSTRACT FROM AUTHOR]

We develop a novel framework, named as $l$ -injection, to address the sparsity problem of recommender systems. By carefully injecting low values to a selected set of unrated user-item pairs in a user-item matrix, we demonstrate that top-Nrecommendation accuracies of various collaborative filtering (CF) techniques can be significantly and consistently improved. We first adopt the notion ofpre-use preferencesof users toward a vast amount ofunrateditems. Using this notion, we identifyuninterestingitems that have not been rated yet but are likely to receive low ratings from users, and selectively impute them as low values. As our proposed approach is method-agnostic, it can be easily applied to a variety of CF algorithms. Through comprehensive experiments with three real-life datasets (e.g., Movielens, Ciao, and Watcha), we demonstrate that our solution consistently and universally enhances the accuracies of existing CF algorithms (e.g., item-based CF, SVD-based CF, and SVD++) by 2.5 to 5 times on average. Furthermore, our solution improves the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy. The datasets and codes that we used in the experiments are available at: https://goo.gl/KUrmip. [ABSTRACT FROM AUTHOR]

Item-to-item collaborative filtering (aka.item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a user's profile as her historically interacted items, recommending new items that are similar to the user's profile. As such, the key to an item-based CF method is in the estimation of item similarities. Early approaches use statistical measures such as cosine similarity and Pearson coefficient to estimate item similarities, which are less accurate since they lack tailored optimization for the recommendation task. In recent years, several works attempt to learn item similarities from data, by expressing the similarity as an underlying model and estimating model parameters by optimizing a recommendation-aware objective function. While extensive efforts have been made to use shallow linear models for learning item similarities, there has been relatively less work exploring nonlinear neural network models for item-based CF. In this work, we propose a neural network model namedNeural Attentive Item Similarity model(NAIS) for item-based CF. The key to our design of NAIS is an attention network, which is capable of distinguishing which historical items in a user profile are more important for a prediction. Compared to the state-of-the-art item-based CF methodFactored Item Similarity Model(FISM) , our NAIS has stronger representation power with only a few additional parameters brought by the attention network. Extensive experiments on two public benchmarks demonstrate the effectiveness of NAIS. This work is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF) is out of question the most widely adopted and successful recommendation approach. A typical CF-based recommender system associates a user with a group of like-minded users based on their individual preferences over all the items, either explicit or implicit, and then recommends to the user some unobserved items enjoyed by the group. However, we find that two users with similar tastes on one item subset may have totally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more reasonable to predict preferences through one user's correlated subgroups, but not the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate a new Multiclass Co-Clustering (MCoC) model, which captures relations of user-to-item, user-to-user, and item-to-item simultaneously. Then, we combine traditional CF algorithms with subgroups for improving their top- $N$ recommendation performance. Our approach can be seen as a new extension of traditional clustering CF models. Systematic experiments on several real data sets have demonstrated the effectiveness of our proposed approach. [ABSTRACT FROM PUBLISHER]

We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques. [ABSTRACT FROM PUBLISHER]

Recommender systems are promising for providing personalized favorite services. Collaborative filtering (CF) technologies, making prediction of users’ preference based on users’ previous behaviors, have become one of the most successful techniques to build modern recommender systems. Several challenging issues occur in previously proposed CF methods: 1) most CF methods ignore users’ response patterns and may yield biased parameter estimation and suboptimal performance; 2) some CF methods adopt heuristic weight settings, which lacks a systematical implementation; and 3) the multinomial mixture models may weaken the computational ability of matrix factorization for generating the data matrix, thus increasing the computational cost of training. To resolve these issues, we incorporate users’ response models into the probabilistic matrix factorization (PMF), a popular matrix factorization CF model, to establish the response aware probabilistic matrix factorization (RAPMF) framework. More specifically, we make the assumption on the user response as a Bernoulli distribution which is parameterized by the rating scores for the observed ratings while as a step function for the unobserved ratings. Moreover, we speed up the algorithm by a mini-batch implementation and a crafting scheduling policy. Finally, we design different experimental protocols and conduct systematical empirical evaluation on both synthetic and real-world datasets to demonstrate the merits of the proposed RAPMF and its mini-batch implementation. [ABSTRACT FROM AUTHOR]

This paper focuses on the measure of recommendation stability, which reflects the consistency of recommender system predictions. Stability is a desired property of recommendation algorithms and has important implications on users’ trust and acceptance of recommendations. Prior research has reported that some popular recommendation algorithms can suffer from a high degree of instability. In this study, we explore two scalable, general-purpose meta-algorithmic approaches—based on bagging and iterative smoothing—that can be used in conjunction with different traditional recommendation algorithms to improve their stability. Our experimental results on real-world rating data demonstrate that both approaches can achieve substantially higher stability as compared to the original recommendation algorithms. Furthermore, perhaps as importantly, the proposed approaches not only do not sacrifice the predictive accuracy in order to improve recommendation stability, but are actually able to provide additional accuracy improvements. [ABSTRACT FROM AUTHOR]

Due to its successful application in recommender systems, collaborative filtering (CF) has become a hot research topic in data mining and information retrieval. In traditional CF methods, only the feedback matrix, which contains either explicit feedback (also called ratings) or implicit feedback on the items given by users, is used for training and prediction. Typically, the feedback matrix is sparse, which means that most users interact with few items. Due to this sparsity problem, traditional CF with only feedback information will suffer from unsatisfactory performance. Recently, many researchers have proposed to utilize auxiliary information, such as item content (attributes), to alleviate the data sparsity problem in CF. Collaborative topic regression (CTR) is one of these methods which has achieved promising performance by successfully integrating both feedback information and item content information. In many real applications, besides the feedback and item content information, there may exist relations (also known as networks) among the items which can be helpful for recommendation. In this paper, we develop a novel hierarchical Bayesian model called Relational Collaborative Topic Regression (RCTR), which extends CTR by seamlessly integrating the user-item feedback information, item content information, and network structure among items into the same model. Experiments on real-world datasets show that our model can achieve better prediction accuracy than the state-of-the-art methods with lower empirical training time. Moreover, RCTR can learn good interpretable latent structures which are useful for recommendation. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas of object typicality from cognitive psychology and propose a novel typicality-based collaborative filtering recommendation method named TyCo. A distinct feature of typicality-based CF is that it finds "neighbors" of users based on user typicality degrees in user groups (instead of the corated items of users, or common users of items, as in traditional CF). To the best of our knowledge, there has been no prior work on investigating CF recommendation by combining object typicality. TyCo outperforms many CF recommendation methods on recommendation accuracy (in terms of MAE) with an improvement of at least 6.35 percent in Movielens data set, especially with sparse training data (9.89 percent improvement on MAE) and has lower time cost than other CF methods. Further, it can obtain more accurate predictions with less number of big-error predictions. [ABSTRACT FROM PUBLISHER]

Recent years have witnessed an increased interest in recommender systems. Despite significant progress in this field, there still remain numerous avenues to explore. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To that end, in this paper, we first analyze the characteristics of the existing travel packages and develop a tourist-area-season topic (TAST) model. This TAST model can represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes. Then, based on this topic model representation, we propose a cocktail approach to generate the lists for personalized travel package recommendation. Furthermore, we extend the TAST model to the tourist-relation-area-season topic (TRAST) model for capturing the latent relationships among the tourists in each travel group. Finally, we evaluate the TAST model, the TRAST model, and the cocktail recommendation approach on the real-world travel package data. Experimental results show that the TAST model can effectively capture the unique characteristics of the travel data and the cocktail approach is, thus, much more effective than traditional recommendation techniques for travel package recommendation. Also, by considering tourist relationships, the TRAST model can be used as an effective assessment for travel group formation. [ABSTRACT FROM AUTHOR]

It is important for online stores to improve customer lifetime value (LTV) if they are to increase their profits. Conventional recommendation methods suggest items that best coincide with user's interests to maximize the purchase probability, and this does not necessarily help improve LTV. We present a novel recommendation method that maximizes the probability of the LTV being improved, which can apply to both measured and subscription services. Our method finds frequent purchase patterns among high-LTV users and recommends items for a new user that simulate the found patterns. Using survival analysis techniques, we efficiently find the patterns from log data. Furthermore, we infer a user's interests from the purchase history based on maximum entropy models and use the interests to improve recommendation. Since a higher LTV is the result of greater user satisfaction, our method benefits users as well as online stores. We evaluate our method using two sets of real log data for measured and subscription services. [ABSTRACT FROM AUTHOR]

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations. [ABSTRACT FROM AUTHOR]

Memory-based collaborative filtering (CF) has been studied extensively in the literature and has proven to be successful in various types of personalized recommender systems. In this paper, we develop a probabilistic framework for memory-based CF (PMCF). While this framework has clear links with classical memory-based CF, it allows us to find principled solutions to known problems of CF-based recommender systems. In particular, we show that a probabilistic active learning method can be used to actively query the user, thereby solving the "new user problem." Furthermore, the probabilistic framework allows us to reduce the computational cost of memory-based CF by working on a carefully selected subset of user profiles, while retaining high accuracy. We report experimental results based on two real-world data sets, which demonstrate that our proposed PMCF framework allows an accurate and efficient prediction of user preferences. [ABSTRACT FROM AUTHOR]

In this paper, we describe the problem of recommending conference sessions to attendees and show how novel extensions to traditional model-based recommender systems, as suggested in Adomavicius and Tuzhilin [CHECK END OF SENTENCE], can address this problem. We introduce Recommendation Engine by CONjoint Decomposition of ITems and USers (RECONDITUS)—a technique that is an extension of preference-based recommender systems to recommend items from a new disjoint set to users from a new disjoint set. The assumption being that preferences exhibited by users with known usage behavior (e.g., past conference session attendance), which can be abstracted by projections of user and item matrices, will be similar to ones of new (different) users where the basic environment and item domain are the same (e.g., new conference). RECONDITUS requires no item ratings, but operates on observed user behavior such as past conference session attendance. The RECONDITUS paradigm consists of projections of both user and item data, and the learning of relationships in projected space. Once established, the relationships enable predicting new relationships and provide associated recommendations. The approach can encompass several traditional data mining problems where both clustering and prediction are necessary. RECONDITUS has been evaluated using data from the Oracle OpenWorld conference. [ABSTRACT FROM AUTHOR]

Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating data sets and different rating prediction algorithms. [ABSTRACT FROM PUBLISHER]

Automated Collaborative Filtering (ACF) refers to a group of algorithms used in recommender systems, a research topic that has received considerable attention due to its e-commerce applications. However, existing techniques are rarely capable of dealing with imperfections in user-supplied ratings. When such imperfections (e.g., ambiguities) cannot be avoided, designers resort to simplifying assumptions that impair the system's performance and utility. We have developed a novel technique referred to as CoFiDS—Collaborative Filtering based on Dempster-Shafer belief-theoretic framework—that can represent a wide variety of data imperfections, propagate them throughout the decision-making process without the need to make simplifying assumptions, and exploit contextual information. With its DS-theoretic predictions, the domain expert can either obtain a "hard” decision or can narrow the set of possible predictions to a smaller set. With its capability to handle data imperfections, CoFiDS widens the applicability of ACF to such critical and sensitive domains as medical decision support systems and defense-related applications. We describe the theoretical foundation of the system and report experiments with a benchmark movie data set. We explore some essential aspects of CoFiDS' behavior and show that its performance compares favorably with other ACF systems. [ABSTRACT FROM AUTHOR]