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We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing ‘like’ or ‘dislike’ feedback. Each user may be recommended a given item at most once. A latent variable model specifies the user preferences: both users and items are clustered into types. All users of a given type have identical preferences for the items, and similarly, items of a given type are either all liked or all disliked by a given user. We assume that the matrix encoding the preferences of each user type for each item type is randomly generated; in this way, the model captures structure in both the item and user spaces, the amount of structure depending on the number of each of the types. The measure of performance of the recommendation system is the expected number of disliked recommendations per user, defined as expected regret. We propose two algorithms inspired by user-user and item-item collaborative filtering (CF), modified to explicitly make exploratory recommendations, and prove performance guarantees in terms of their expected regret. For two regimes of model parameters, with structure only in item space or only in user space, we prove information-theoretic lower bounds on regret that match our upper bounds up to logarithmic factors. Our analysis elucidates system operating regimes in which existing CF algorithms are nearly optimal. [ABSTRACT FROM AUTHOR]

Recovering low-rank matrices from incomplete observations is a fundamental problem with many applications, especially in recommender systems. In theory, under certain conditions, this problem can be solved by convex or nonconvex relaxation. However, most existing provable algorithms suffer from superlinear per-iteration cost, which severely limits their applicability to large-scale problems. In this paper, we propose a novel fuzzy double trace norm minimization (DTNM) method for recommender systems. We first present a tractable DTNM model, in which we can integrate both the user social relationship and the user reputation information using a fuzzy weighting way and coupling fuzzy matrix factorization. In essence, our model is a Schatten- ${1/2}$ quasi-norm minimization problem. Moreover, we develop two efficient augmented Lagrangian algorithms to solve the proposed problems, and prove the convergence of our algorithms. Finally, we investigate the empirical recoverability properties of our model and its advantage over classical trace norm. Extensive experimental results on both synthetic and real-world data sets verified both the efficiency and effectiveness of our method compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In recent years, recommender system is one of indispensable components in many e-commerce websites. One of the major challenges that largely remains open is the cold-start problem, which can be viewed as a barrier that keeps the cold-start users/items away from the existing ones. In this paper, we aim to break through this barrier for cold-start users/items by the assistance of existing ones. In particular, inspired by the classic Elo Rating System, which has been widely adopted in chess tournaments, we propose a novel rating comparison strategy (RaPare ) to learn the latent profiles of cold-start users/items. The centerpiece of our RaPare is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and existing users/items. As a generic strategy, our proposed strategy can be instantiated into existing methods in recommender systems. To reveal the capability of RaPare strategy, we instantiate our strategy on two prevalent methods in recommender systems, i.e., the matrix factorization based and neighborhood based collaborative filtering. Experimental evaluations on five real data sets validate the superiority of our approach over the existing methods in cold-start scenario. [ABSTRACT FROM PUBLISHER]

Personalized review-based rating prediction, a newly emerged research problem, aims at inferring users’ ratings over their unrated items using existing reviews and corresponding ratings. While some researchers proposed to learn topic factor from review text to obtain interpretability for rating prediction, they often overlooked the fact that the learned topic factors are limited to review text and cannot fully reveal the complicated relations between reviews and ratings. Moreover, topic modeling based solutions for this problem usually utilize Gibbs sampling algorithms to learn topics and word distributions, resulting in non-negligible computational overload. To address the above challenges, we propose an integrated topic and latent factor model (ITLFM), which combines topic and latent factors in a linear way to make them complement each other for better accuracies in rating prediction tasks. In addition, ITLFM models review text through an additive topic model to reveal user's and item's topic factors simultaneously. To ensure high learning efficiency, we design a hybrid stochastic learning algorithm for ITLFM. We evaluate ITLFM on several standard benchmarks and compare with representative approaches. The experimental results demonstrate that the proposed ITLFM method is computationally efficient and accurate, as well as scalable for large scale applications. [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]

Various memory- and model-based collaborative filtering algorithms have been designed for multiclass feedback (such as grade scores) in the past two decades. Recently, one-class feedback (such as positive feedback and implicit examination) has been recognized as a more pervasive and important source of information in many real recommendation systems. Previous work along these lines mainly focus on homogenous one-class positive feedback, such as likes on Facebook or transactions on Amazon, which might not capture a user's true preferences due to the sparsity of such data. To alleviate this sparsity problem, the authors study positive feedback and implicit examinations simultaneously, coined as heterogeneous one-class collaborative filtering (HOCCF). Specifically, they designed a novel transfer learning algorithm for HOCCF, called transfer via joint similarity learning (TJSL), that jointly learns a similarity between a candidate item and a preferred item, and a similarity between a candidate item and an identified likely-to-prefer examined item. Joint similarity learning has the merit of being able to connect two seemingly unrelated items along sparse positive feedback only. Empirical studies on three real-world datasets show that TJSL can recommend items more accurately than other state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

The weighted bipartite $b$ -matching problem (WBM) plays a significant role in many real-world applications, including resource allocation, scheduling, Internet advertising, and E-commerce. WBM has been widely studied and efficient matching algorithms are well known. In this work, we study a novel variant of WBM, called conflict-aware WBM (CA-WBM), where conflict constraints are present between vertices of the bipartite graph. In CA-WBM, if two vertices (on the same side) are in conflict, they may not be included in the matching result simultaneously. We present a generalized formulation of CA-WBM in the context of E-commerce, where diverse matching results are often desired (e.g., movies of different genres and merchants selling products of different categories). While WBM is efficiently solvable in polynomial-time, we show that CA-WBM is NP-hard. We propose approximate and randomized algorithms to solve CA-WBM and show that they achieve close to optimal solutions via comprehensive experiments using synthetic datasets. We derive a theoretical bound on the approximation ratio of a greedy algorithm for CA-WBM and show that it is scalable on a large-scale real-world dataset. [ABSTRACT FROM PUBLISHER]

Low-rank matrix completion (LRMC) problems arise in a wide variety of applications. Previous theory mainly provides conditions for completion under missing-at-random samplings. This paper studies deterministic conditions for completion. An incomplete $d \times N$ matrix is finitely rank-r$ completable if there are at most finitely many rank-r$ matrices that agree with all its observed entries. Finite completability is the tipping point in LRMC, as a few additional samples of a finitely completable matrix guarantee its unique completability. The main contribution of this paper is a characterization of finitely completable observation sets. We use this characterization to derive sufficient deterministic sampling conditions for unique completability that can be efficiently verified. We also show that under uniform random sampling schemes, these conditions are satisfied with high probability if entries per column are observed. These findings have several implications on LRMC regarding lower bounds, sample and computational complexity, the role of coherence, adaptive settings, and the validation of any completion algorithm. We complement our theoretical results with experiments that support our findings and motivate future analysis of uncharted sampling regimes. [ABSTRACT FROM PUBLISHER]

Recommender systems have become an invaluable asset to online services with the ever-growing number of items and users. Most systems focused on recommendation accuracy, predicting likable items for each user. Such methods tend to generate popular and safe recommendations, but fail to introduce users to potentially risky, yet novel items that could help in increasing the variety of items consumed by the users. This is known as popularity bias, which is predominant in methods that adopt collaborative filtering. Recently, however, recommenders have started to improve their methods to generate lists that encompass diverse items that are both accurate and novel through specific novelty-driven algorithms or hybrid recommender systems. In this paper, we propose a recommender system that uses the concepts of Experts to find both novel and relevant recommendations. By analyzing the ratings of the users, the algorithm promotes special Experts from the user population to create novel recommendations for a target user. Thus, different users are promoted dynamically to Experts depending on who the recommendations are for. The system used data collected from Last.fm and was evaluated with several metrics. Results show that the proposed system outperforms matrix factorization methods in finding novel items and performs on par in finding simultaneously novel and relevant items. This system can also provide a means to popularity bias while preserving the advantages of collaborative filtering. [ABSTRACT FROM PUBLISHER]

Web services are integrated software components for the support of interoperable machine-to-machine interaction over a network. Web services have been widely employed for building service-oriented applications in both industry and academia in recent years. The number of publicly available Web services is steadily increasing on the Internet. However, this proliferation makes it hard for a user to select a proper Web service among a large amount of service candidates. An inappropriate service selection may cause many problems (e.g., ill-suited performance) to the resulting applications. In this paper, we propose a novel collaborative filtering-based Web service recommender system to help users select services with optimal Quality-of-Service (QoS) performance. Our recommender system employs the location information and QoS values to cluster users and services, and makes personalized service recommendation for users based on the clustering results. Compared with existing service recommendation methods, our approach achieves considerable improvement on the recommendation accuracy. Comprehensive experiments are conducted involving more than 1.5 million QoS records of real-world Web services to demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Domain analysis is a labor-intensive task in which related software systems are analyzed to discover their common and variable parts. Many software projects include extensive domain analysis activities, intended to jumpstart the requirements process through identifying potential features. In this paper, we present a recommender system that is designed to reduce the human effort of performing domain analysis. Our approach relies on data mining techniques to discover common features across products as well as relationships among those features. We use a novel incremental diffusive algorithm to extract features from online product descriptions, and then employ association rule mining and the $(k)$-nearest neighbor machine learning method to make feature recommendations during the domain analysis process. Our feature mining and feature recommendation algorithms are quantitatively evaluated and the results are presented. Also, the performance of the recommender system is illustrated and evaluated within the context of a case study for an enterprise-level collaborative software suite. The results clearly highlight the benefits and limitations of our approach, as well as the necessary preconditions for its success. [ABSTRACT FROM PUBLISHER]

The social web presents new opportunities for e-commerce and services provided to the consumer. This paper presents an online recommendation system that eases the matching of a user with the most relevant products and services. The designed system consists of three recommendation algorithms; two of them are already widely discussed in the literature, while the third one is new and was proposed to consider the trust relationship between users and their peers. This work also presents an experiment with real users that intends to evaluate the proposed new algorithm in comparison with the other two and also with direct, human-to-human recommendations. The paper presents the results gathered on experimental data analysis and the statistical hypotheses tests that were performed, which allowed concluding in which circumstances trust-based recommendation is advantageous. [ABSTRACT FROM PUBLISHER]

This paper proposes a content-based artist recommendation framework which learns relationships between users' preference and music contents through ordinal regression. In particular, an artist is characterized by the parameters of its corresponding acoustical model which is adapted from a universal background model. These artist-specific acoustic features together with their preference rankings are then used as input vectors for the proposed order preserving projection (OPP) algorithm which tries to find a suitable subspace such that the desired ranking order of the data after projection can be kept as much as possible. The proposed linear OPP can be kernelized to learn the nonlinear relationship between music contents and users' artist rank orders. Under the proposed framework of kernelized OPP (KOPP), we can derive the nonlinear relationship and, more importantly, efficiently fuse acoustic and symbolic features obtained from the artist recommended meta-data. Experimental results demonstrate that OPP attains comparable results with those obtained with a conventional ordinal regression method, Prank. Moreover, by exploring the nonlinear relationship among training examples and combining acoustic and symbolic features, KOPP outperforms previous approaches to artist recommendation. [ABSTRACT FROM PUBLISHER]

Using song files shared by users in the Gnutella network, the authors present a method for constructing a song-similarity graph to create scalable and efficient applications. [ABSTRACT FROM PUBLISHER]