In this paper, we propose a Domain-sensitive Recommendation (DsRec) algorithm, to make the rating prediction by exploring the user-item subgroup analysis simultaneously, in which a user-item subgroup is deemed as a domain consisting of a subset of items with similar attributes and a subset of users who have interests in these items. The proposed framework of DsRec includes three components: a matrix factorization model for the observed rating reconstruction, a bi-clustering model for the user-item subgroup analysis, and two regularization terms to connect the above two components into a unified formulation. Extensive experiments on three real-world datasets show that our method achieves the better performance over some state-of-the-art methods.

Collaborative Filtering (CF) is one of the most successful recommendation approaches to cope with information overload in the real world. However, typical CF methods equally treat every user and item, and cannot distinguish the variation of user's interests across different domains. This violates the reality that user's interests always center on some specific domains, and the users having similar tastes on one domain may have totally different tastes on another domain. Motivated by the observation, in this paper, we propose a novel Domain-sensitive Recommendation (DsRec) algorithm, to make the rating prediction by exploring the user-item subgroup analysis simultaneously, in which a user-item subgroup is deemed as a domain consisting of a subset of items with similar attributes and a subset of users who have interests in these items. The proposed framework of DsRec includes three components: a matrix factorization model for the observed rating reconstruction, a bi-clustering model for the user-item subgroup analysis, and two regularization terms to connect the above two components into a unified formulation. Extensive experiments on Movielens-100K and two real-world product review datasets show that our method achieves the better performance in terms of prediction accuracy criterion over the state-of-the-art methods.

Recently, hashing based approximate nearest neighbors search has attracted much attention. Extensive centralized hashing algorithms have been proposed and achieved promising performance. However, due to the large scale of many applications, the data is often stored or even collected in a distributed manner. Learning hash functions by aggregating all the data into a fusion center is infeasible because of the prohibitively expensive communication and computation overhead. In this paper, we develop a novel hashing model to learn hash functions in a distributed setting. We cast a centralized hashing model as a set of subproblems with consensus constraints. We find these subproblems can be analytically solved in parallel on the distributed compute nodes. Since no training data is transmitted across the nodes in the learning process, the communication cost of our model is independent to the data size. Extensive experiments on several large scale datasets containing up to 100 million samples demonstrate the efficacy of our method.

Existing recommender systems place emphasis on personalization to achieve promising accuracy. However, in the context of multiple domain, users are likely to seek the same behaviors as domain authorities. This conformity effect provides a wealth of prior knowledge when it comes to multi-domain recommendation, but has not been fully exploited. In particular, users whose behaviors are significant similar with the public tastes can be viewed as domain authorities. To detect these users meanwhile embed conformity into recommendation, a domain-specific similarity matrix is intuitively employed. Therefore, a collective similarity is obtained to leverage the conformity with personalization. In this paper, we establish a Collective Structure Sparse Representation(CSSR) method for multi-domain recommendation. Based on adaptive $k$-Nearest-Neighbor framework, we impose the lasso and group lasso penalties as well as least square loss to jointly optimize the collective similarity. Experimental results on real-world data confirm the effectiveness of the proposed method.

Recently, some recommendation methods try to improvethe prediction results by integrating informationfrom user’s multiple types of behaviors. How to modelthe dependence and independence between differentbehaviors is critical for them. In this paper, we proposea novel recommendation model, the Group-Sparse MatrixFactorization (GSMF), which factorizes the ratingmatrices for multiple behaviors into the user and itemlatent factor space with group sparsity regularization.It can (1) select out the different subsets of latent factorsfor different behaviors, addressing that users’ decisionson different behaviors are determined by differentsets of factors;(2) model the dependence and independencebetween behaviors by learning the sharedand private factors for multiple behaviors automatically; (3) allow the shared factors between different behaviorsto be different, instead of all the behaviors sharingthe same set of factors. Experiments on the real-world dataset demonstrate that our model can integrate users’multiple types of behaviors into recommendation better,compared with other state-of-the-arts.

Due to the ability to preserve semantic similarity in Hamming space, supervised hashing has been extensively studied recently. Most existing approaches encourage two dissimilar samples to have maximum Hamming distance. This may lead to an unexpected consequence that two unnecessarily similar samples would have the same code if they are both dissimilar with another sample. Besides, in existing methods, all labeled pairs are treated with equal importance without considering the semantic gap, which is not conducive to thoroughly leverage the supervised information. We present a general framework for supervised hashing to address the above two limitations. We do not toughly require a dissimilar pair to have maximum Hamming distance. Instead, a soft constraint which can be viewed as a regularization to avoid over-fitting is utilized. Moreover, we impose different weights to different training pairs, and these weights can be automatically adjusted in the learning process. Experiments on two benchmarks show that the proposed method can easily outperform other state-of-the-art methods.

Recommendation for new users is a key challenge due to the lack of prior information from them, which is the well-known cold-start problem. Preference elicitation has been proposed as an efficient strategy for eliciting new users preference through an initial interview where new users are queried by elaborately selected items. In this paper, we propose a novel community discovering guided discriminative selection (CDDS) model for constructing query set. We exploit the community as an effective information which is not fully used in existing approaches. By integrating item selection and community discovery into one framework, our model selects most discriminative items for preference elicitation, with guidance of unsupervised community discovering process. To perform community discovering process, the model utilizes rating similarity graph and social network as a graph regular-ization. Experimental results on real-world datasets Flixster and Douban demonstrate that the proposed method outperforms traditional preference elicitation methods for cold-start recommendation.

Recommendation for cold users is fairly challenging because no prior rating can be used in preference prediction. To tackle this cold-start scenario, rating elicitation is usually employed through an initial interview in which users are queried by some carefully selected items. In this paper, we propose a novel framework to mine the most valuable items to construct query set using a semi-supervised discriminative selection (SSDS) model. To learn a low dimensional representation for users in item space which can reflect their tastes to a large extent, the model incorporates category labels as discriminative information. To ensure the used labels reliable as well as all users considered, the model utilizes a semi-supervised scheme leveraging expert guidance with graph regularization. Experimental results on real-world dataset MovieLens demonstrate that the proposed SSDS model outperforms traditional preference elicitation methods on top-N measures for cold-start recommendation.

Traditionally, Collaborative Filtering assumes that similar users have similar responses to similar items. However, human activities exhibit heterogenous features across multiple domains such that users own similar tastes in one domain may behave quite differently in other domains. Moreover, highly sparse data presents crucial challenge in preference prediction. Intuitively, if users' interested domains are captured first, the recommender system is more likely to provide the enjoyed items while filter out those uninterested ones. Therefore, it is necessary to learn preference profiles from the correlated domains instead of the entire user-item matrix. In this paper, we propose a unified framework, TopRec, which detects topical communities to construct interpretable domains for domain-specific collaborative filtering. In order to mine communities as well as the corresponding topics, a semi-supervised probabilistic topic model is utilized by integrating user guidance with social network. Experimental results on real-world data from Epinions and Ciao demonstrate the effectiveness of the proposed framework.