In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations. Some user-centric tasks, such as image recommendations, call for effective representations of not only images but also preferences and intents of users over images. Such representations are termed hybrid and addressed via a deep learning approach in this paper. We design a dual-net deep network, in which the two sub-networks map input images and preferences of users into a same latent semantic space, and then the distances between images and users in the latent space are calculated to make decisions. We further propose a comparative deep learning (CDL) method to train the deep network, using a pair of images compared against one user to learn the pattern of their relative distances. The CDL embraces much more training data than naive deep learning, and thus achieves superior performance than the latter, with no cost of increasing network complexity. Experimental results with real-world data sets for image recommendations have shown the proposed dual-net network and CDL greatly outperform other state-of-the-art image recommendation solutions.
Only a few recent work is concentrated on joint modeling of users and images for making recommendations @cite_8 @cite_17 @cite_1 . Sang al @cite_1 propose a topic sensitive model that concerns user preferences and user uploaded images to study users' influences in social networks. Liu al @cite_17 propose to recommend images by voting strategy according to learnt social embedded image representations. Till now, the existing methods often perform separate processing of user information and image and then simply combining them. A fully integrated solution is to be investigated.
@cite_1 @cite_17 @cite_8
2210549170 2151751257 2020600820 57543762
Good representations of data do help in many machine learning tasks such as recommendation. It is often a great challenge for traditional recommender systems to learn representative features of both users and images in large social networks, in particular, social curation networks, which are characterized as the extremely sparse links between users and images, and the extremely diverse visual contents of images. To address the challenges, we propose a novel deep model which learns the unified feature representations for both users and images. This is done by transforming the heterogeneous user-image networks into homogeneous low-dimensional representations, which facilitate a recommender to trivially recommend images to users by feature similarity. We also develop a fast online algorithm that can be easily scaled up to large networks in an asynchronously parallel way. We conduct extensive experiments on a representative subset of Pinterest, containing 1,456,540 images and 1,000,000 users. Results of image recommendation experiments demonstrate that our feature learning approach significantly outperforms other state-of-the-art recommendation methods. Social media is becoming popular these days, where user necessarily interacts with each other to form social networks. Influence network, as one special case of social network, has been recognized as significantly impacting social activities and user decisions. We emphasize in this paper that the inter-user influence is essentially topic-sensitive, as for different tasks users tend to trust different influencers and be influenced most by them. While existing research focuses on global influence modeling and applies to text-based networks, this work investigates the problem of topic-sensitive influence modeling in the multimedia domain. We propose a multi-modal probabilistic model, considering both users' textual annotation and uploaded visual image. This model is capable of simultaneously extracting user topic distributions and topic-sensitive influence strengths. By identifying the topic-sensitive influencer, we are able to conduct applications like collective search and collaborative recommendation. A risk minimization-based general framework for personalized image search is further presented, where the image search task is transferred to measure the distance of image and personalized query language models. The framework considers the noisy tag issue and enables easy incorporation of social influence. We have conducted experiments on a large-scale Flickr dataset. Qualitative as well as quantitative evaluation results have validated the effectiveness of the topic-sensitive influencer mining model, and demonstrated the advantage of incorporating topic-sensitive influence in personalized image search and topic-based image recommendation. Although video recommender systems have become the predominant way for people to obtain video information, their performances are far from satisfactory in that (1) the recommended videos are often mismatched with the users' interests and (2) the recommendation results are, in most cases, hardly understandable for users and therefore cannot persuade them to engage. In this paper, we attempt to address the above problems in data representation level, and aim to learn a common attributed representation for users and videos in social media with good interpretability, stability and an appropriate level of granularity. The basic idea is to represent videos with users' social attributes, and represent users with content attributes of videos, such that both users and videos can be represented in a common space concatenated by social attributes and content attributes. The video recommendation problem can then be converted into a similarity matching problem in the common space. However, it is still challenging to balance the roles of social attributes and content attributes, learn such a common representation in sparse user-video interactions and deal with the cold-start problem. In this paper, we propose a REgularized Dual-fActor Regression (REDAR) method based on matrix factorization. In this method, social attributes and content attributes are flexibly combined, and social and content information are effectively exploited to alleviate the sparsity problem. An incremental version of REDAR is designed to solve the cold-start problem. We extensively evaluate the proposed method for video recommendation application in real social network dataset, and the results show that, in most cases, the proposed method can achieve a relative improvement of more than 20 compared to state-of-the-art baseline methods. In the age of information overload, collaborative filtering and recommender systems have become essential tools for content discovery. The advent of online social networks has added another approach to recommendation whereby the social network itself is used as a source for recommendations i.e. users are recommended items that are preferred by their friends. In this paper we develop a new model-based recommendation method that merges collaborative and social approaches and utilizes implicit feedback and the social graph data. Employing factor models, we represent each user profile as a mixture of his own and his friends' profiles. This assumes and exploits "homophily" in the social network, a phenomenon that has been studied in the social sciences. We test our model on the Epinions data and on the Tuenti Places Recommendation data, a large-scale industry dataset, where it outperforms several state-of-the-art methods.