We tackle the blog recommendation problem in Tumblr for mobile users in this paper. Blog recommendation is challenging since most mobile users would suffer from the cold start when there are only a limited number of blogs followed by the user. Specifically to address this problem in the mobile domain, we take into account mobile apps, which typically provide rich information from the users. Based on the assumption that the user interests can be reflected from their app usage patterns, we propose to exploit the app usage data for improving blog recommendation. Building on the state-of-the-art recommendation framework, Factorization Machines (FM), we implement app-based FM that integrates app usage data with the user-blog follow relations. In this approach the blog recommendation is generated not only based on the blogs that the user followed before, but also the apps that the user has often used. We demonstrate in a series of experiments that app-based FM can outperform other alternative approaches to a significant extent. Our experimental results also show that exploiting app usage information is particularly effective for improving blog recommendation quality for cold start users.
Our work is also related to the research topic of cross-domain recommendation, which generally refers to the recommendation scenarios that involve multiple application domains @cite_8 . Prior work has shown that sharing knowledge between different product domains can bring up mutual benefits. For example, the users may have similar interest in movies and books @cite_15 . It is also shown that auxiliary information sources can be exploited as intermediate for linking different domains, e.g., tags may be common between the book domain and the movie domain @cite_0 , and users may have similar rating patterns across different product domains @cite_15 @cite_7 . We consider that external information sources are critical for addressing the cold start problem for blog recommendation. In particular, our work is specialized in the mobile domain, in which we exploit the rich user feedback from their interactions with mobile apps. Our assumption is that the apps that a user often uses can indicate her underlying interests, which can be leveraged for blog recommendation in Tumblr.
@cite_0 @cite_15 @cite_7 @cite_8
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The behavior of users in certain services could be a clue that can be used to infer their preferences and may be used to make recommendations for other services they have never used. However, the cross-domain relationships between items and user consumption patterns are not simple, especially when there are few or no common users and items across domains. To address this problem, we propose a content-based cross-domain recommendation method for cold-start users that does not require user- and item- overlap. We formulate recommendation as extreme multi-class classification where labels (items) corresponding to the users are predicted. With this formulation, the problem is reduced to a domain adaptation setting, in which a classifier trained in the source domain is adapted to the target domain. For this, we construct a neural network that combines an architecture for domain adaptation, Domain Separation Network, with a denoising autoencoder for item representation. We assess the performance of our approach in experiments on a pair of data sets collected from movie and news services of Yahoo! JAPAN and show that our approach outperforms several baseline methods including a cross-domain collaborative filtering method. Recent online services rely heavily on automatic personalization to recommend relevant content to a large number of users. This requires systems to scale promptly to accommodate the stream of new users visiting the online services for the first time. In this work, we propose a content-based recommendation system to address both the recommendation quality and the system scalability. We propose to use a rich feature set to represent users, according to their web browsing history and search queries. We use a Deep Learning approach to map users and items to a latent space where the similarity between users and their preferred items is maximized. We extend the model to jointly learn from features of items from different domains and user features by introducing a multi-view Deep Learning model. We show how to make this rich-feature based user representation scalable by reducing the dimension of the inputs and the amount of training data. The rich user feature representation allows the model to learn relevant user behavior patterns and give useful recommendations for users who do not have any interaction with the service, given that they have adequate search and browsing history. The combination of different domains into a single model for learning helps improve the recommendation quality across all the domains, as well as having a more compact and a semantically richer user latent feature vector. We experiment with our approach on three real-world recommendation systems acquired from different sources of Microsoft products: Windows Apps recommendation, News recommendation, and Movie TV recommendation. Results indicate that our approach is significantly better than the state-of-the-art algorithms (up to 49 enhancement on existing users and 115 enhancement on new users). In addition, experiments on a publicly open data set also indicate the superiority of our method in comparison with transitional generative topic models, for modeling cross-domain recommender systems. Scalability analysis show that our multi-view DNN model can easily scale to encompass millions of users and billions of item entries. Experimental results also confirm that combining features from all domains produces much better performance than building separate models for each domain. Recommender systems always aim to provide recommendations for a user based on historical ratings collected from a single domain (e.g., movies or books) only, which may suffer from the data sparsity problem. Recently, several recommendation models have been proposed to transfer knowledge by pooling together the rating data from multiple domains to alleviate the sparsity problem, which typically assume that multiple domains share a latent common rating pattern based on the user-item co-clustering. In practice, however, the related domains do not necessarily share such a common rating pattern, and diversity among the related domains might outweigh the advantages of such common pattern, which may result in performance degradations. In this paper, we propose a novel cluster-level based latent factor model to enhance the cross-domain recommendation, which can not only learn the common rating pattern shared across domains with the flexibility in controlling the optimal level of sharing, but also learn the domain-specific rating patterns of users in each domain that involve the discriminative information propitious to performance improvement. To this end, the proposed model is formulated as an optimization problem based on joint nonnegative matrix tri-factorization and an efficient alternating minimization algorithm is developed with convergence guarantee. Extensive experiments on several real world datasets suggest that our proposed model outperforms the state-of-the-art methods for the cross-domain recommendation task. Given the vast amount of information on the World Wide Web, recommender systems are increasingly being used to help filter irrelevant data and suggest information that would interest users. Traditional systems make recommendations based on a single domain e.g., movie or book domain. Recent work has examined the correlations in different domains and designed models that exploit user preferences on a source domain to predict user preferences on a target domain. However, these methods are based on matrix factorization and can only be applied to two-dimensional data. Transferring high dimensional data from one domain to another requires decomposing the high dimensional data to binary relations which results in information loss. Furthermore, this decomposition creates a large number of matrices that need to be transferred and combining them in the target domain is non-trivial. Separately, researchers have looked into using social network information to improve recommendation. However, this social network information has not been explored in cross domain collaborative filtering. In this work, we propose a generalized cross domain collaborative filtering framework that integrates social network information seamlessly with cross domain data. This is achieved by utilizing tensor factorization with topic based social regularization. This framework is able to transfer high dimensional data without the need for decomposition by finding shared implicit cluster-level tensor from multiple domains. Extensive experiments conducted on real world datasets indicate that the proposed framework outperforms state-of-art algorithms for item recommendation, user recommendation and tag recommendation.