The new user problem (aka user cold start) is very common in online recommender systems. Active collaborative filtering (active CF) tries to solve this problem by intelligently soliciting user feedback in order to build an initial user profile with minimal costs. Existing methods only query the user for feedback on items, while users can have preferences over items as well as certain item attributes. In this paper, we extend active CF via user feedback on both items and attributes. For example, when making movie recommendations, the system can ask users for not only their favorite movies, but also attributes such as genres, actors, etc. We design a unified active CF framework for incorporating both item and attribute feedback based on the random walk model. We test the active CF algorithm on real-world movie recommendation data sets to demonstrate that appropriately querying for both item and feature feedback can significantly reduce the overall user effort measured in terms of number of queries. We show that we can achieve much better recommendation quality as compared to traditional active CF methods that support only item feedback.

We propose a novel probabilistic model for collaborative filtering (CF), called SRMCoFi, which seamlessly integrates both linear and bilinear random effects into a principled framework. The formulation of SRMCoFi is supported by both social psychological experiments and statistical theories. Not only can many existing CF methods be seen as special cases of SRMCoFi, but it also integrates their advantages while simultaneously overcoming their disadvantages. The solid theoretical foundation of SRMCoFi is further supported by promising empirical results obtained in extensive experiments using real CF data sets on movie ratings.

Advanced e-applications require comprehensive knowledge about their users’ preferences in order to provide accurate personalized services. In this paper, we propose to learn users’ preferences to product brands from their implicit feedbacks such as their searching and browsing behaviors in user Web browsing log data. The user brand preference learning problem is challenge since (1) the users’ implicit feedbacks are extremely sparse in various product domains; and (2) we can only observe positive feedbacks from users’ behaviors. In this paper, we propose a latent factor model to collaboratively mine users’ brand preferences across multiple domains simultaneously. By collective learning, the learning processes in all the domains are mutually enhanced and hence the problem of data scarcity in each single domain can be effectively addressed. On the other hand, we learn our model with an adaption of the Bayesian personalized ranking (BPR) optimization criterion which is a general learning framework for collaborative filtering from implicit feedbacks. Experiments with both synthetic and real world datasets show that our proposed model significantly outperforms the baselines.

The most critical challenge for the recommendation system is to achieve the high prediction quality on the large scale sparse data contributed by the users. In this paper, we present a novel approach to the social recommendation problem, which takes the advantage of the graph Laplacian regularization to capture the underlying social relationship among the users. Differently from the previous approaches, that are based on the conventional gradient descent optimization, we formulate the presented graph Laplacian regularized social recommendation problem into a low-rank semidefinite program, which is able to be efficiently solved by the quasi-Newton algorithm. We have conducted the empirical evaluation on a large scale dataset of high sparsity, the promising experimental results show that our method is very effective and efficient for the social recommendation task.

In real-world recommender systems, some users are easily influenced by new products and whereas others are unwilling to change their minds. So the preference varying speeds for users are different. Based on this observation, we propose a dynamic nonlinear matrix factorization model for collaborative filtering, aimed to improve the rating prediction performance as well as track the preference varying speeds for different users. We assume that user-preference changes smoothly over time, and the preference varying speeds for users are different. These two assumptions are incorporated into the proposed model as prior knowledge on user feature vectors, which can be learned efficiently by MAP estimation. The experimental results show that our method not only achieves state-of-the-art performance in the rating prediction task, but also provides an effective way to track user-preference varying speed.

Subjective assessments (SAs) are assigned by users against items, such as ’elegant’ and ’gorgeous’, and are common in reviews/tags in many online-sites. However, previous studies fail to effectively use SAs for improving recommendations because few users rate the same items with the same SAs, which triggers the sparsity problem in collaborative filtering. We propose a novel algorithm that links a taxonomy of items to a taxonomy of SAs to assess user interests in detail. That is, it merges the SAs assigned by users against an item into subjective classes (SCs) and reflects the SAs/SCs assigned to an item to its classes. Thus, it can measure the similarity of users from not only SAs/SCs assigned to items but also their classes, which overcomes the sparsity problem. Our evaluation, which uses data from a popular restaurant review site, shows that our method generates more accurate recommendations than previous methods. Furthermore, we find that SAs frequently assigned on a few item classes are more useful than those widely assigned against many item classes in terms of recommendation accuracy.

Most of the existing personalization systems such as content recommenders or targeted ads focus on individual users and ignore the social situation in which the services are consumed. However, many human activities are social and involve several in-dividuals whose tastes and expectations must be taken into account by the system. When a group profile is not available, different profile aggrega-tion strategies can be applied to recommend ade-quate items to a group of users based on their indi-vidual profiles. We consider an approach intended to determine the factors that influence the choice of an aggregation strategy. We present evaluations made on a large-scale dataset of TV viewings, where real group interests are compared to the pre-dictions obtained by combining individual user profiles according to different strategies.

There are common intuitions about how social graphs are generated (for example, it is common to talk informally about nearby nodes sharing a link). There are also common heuristics for predicting whether two currently unlinked nodes in a graph should be linked (e.g. for suggesting friends in an online social network or movies to customers in a recommendation network). This paper provides what we believe to be the first formal connection between these intuitions and these heuristics. We look at a familiar class of graph generation models in which nodes are associated with locations in a latent metric space and connections are more likely between closer nodes. We also look at popular linkprediction heuristics such as number-of-commonneighbors and its weighted variants [Adamic and Adar, 2003] which have proved successful in predicting missing links, but are not direct derivatives of latent space graph models. We provide theoretical justifications for the success of some measures as compared to others, as reported in previous empirical studies. In particular we present a sequence of formal results that show bounds related to the role that a node’s degree plays in its usefulness for link prediction, the relative importance of short paths versus long paths, and the effects of increasing non-determinism in the link generation process on link prediction quality. Our results can be generalized to any model as long as the latent space assumption holds.

The personalization aspect of recommender systems makes them well suited to applications in The goal of rating-based recommender systems is electronic commerce and entertainment, while the fact that to make personalized predictions and recommendations they do not rely on text-based descriptions of items makes for individual users by leveraging the preferences them well suited to content like movies and music. of a community of users with respect to a In this paper, we focus on a key problem in rating-based collection of items like songs or movies. Recommender collaborative filtering: the possibility of a basic incompatibility systems are often based on intricate statistical between the properties of recommender system data sets models that are estimated from data sets containing and the assumptions required for valid estimation and evaluation a very high proportion of missing ratings. of statistical models in the presence of missing data. This work describes evidence of a basic incompatibility We describe properties of recommender system data sets and between the properties of recommender relate them to the statistical theory of model estimation in system data sets and the assumptions required for the presence of nonrandom missing data. We describe an valid estimation and evaluation of statistical models extended modelling framework and a modified set of evaluation in the presence of missing data. We discuss the protocols for dealing with nonrandom missing data.

Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. With the advent of online social networks, the social network based approach to recommendation has emerged. This approach assumes a social network among users and makes recommendations for a user based on the ratings of the users who have direct or indirect social relations with the given user. As one of their major benefits, social network based approaches have been shown to reduce the problems with cold start users. In this paper, we explore a model-based approach for recommendation in social networks, employing matrix factorization techniques. Advancing previous work, we incorporate the mechanism of trust propagation into the model in a principled way. Trust propagation has been shown to be a crucial phenomenon in the social sciences, in social network analysis and in trust-based recommendation. We have conducted experiments on two real life data sets. Our experiments demonstrate that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users.