Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Adversarial Training Towards Robust Multimedia Recommender System

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advance on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR.Comment: TKD

Collaborative video searching on a tabletop

Almost all system and application design for multimedia systems is based around a single user working in isolation to perform some task yet much of the work for which we use computers to help us, is based on working collaboratively with colleagues. Groupware systems do support user collaboration but typically this is supported through software and users still physically work independently. Tabletop systems, such as the DiamondTouch from MERL, are interface devices which support direct user collaboration on a tabletop. When a tabletop is used as the interface for a multimedia system, such as a video search system, then this kind of direct collaboration raises many questions for system design. In this paper we present a tabletop system for supporting a pair of users in a video search task and we evaluate the system not only in terms of search performance but also in terms of user–user interaction and how different user personalities within each pair of searchers impacts search performance and user interaction. Incorporating the user into the system evaluation as we have done here reveals several interesting results and has important ramifications for the design of a multimedia search system

Literature Overview - Privacy in Online Social Networks

In recent years, Online Social Networks (OSNs) have become an important\ud part of daily life for many. Users build explicit networks to represent their\ud social relationships, either existing or new. Users also often upload and share a plethora of information related to their personal lives. The potential privacy risks of such behavior are often underestimated or ignored. For example, users often disclose personal information to a larger audience than intended. Users may even post information about others without their consent. A lack of experience and awareness in users, as well as proper tools and design of the OSNs, perpetuate the situation. This paper aims to provide insight into such privacy issues and looks at OSNs, their associated privacy risks, and existing research into solutions. The final goal is to help identify the research directions for the Kindred Spirits project

On the anonymity risk of time-varying user profiles.

Websites and applications use personalisation services to profile their users, collect their patterns and activities and eventually use this data to provide tailored suggestions. User preferences and social interactions are therefore aggregated and analysed. Every time a user publishes a new post or creates a link with another entity, either another user, or some online resource, new information is added to the user profile. Exposing private data does not only reveal information about single users’ preferences, increasing their privacy risk, but can expose more about their network that single actors intended. This mechanism is self-evident in social networks where users receive suggestions based on their friends’ activities. We propose an information-theoretic approach to measure the differential update of the anonymity risk of time-varying user profiles. This expresses how privacy is affected when new content is posted and how much third-party services get to know about the users when a new activity is shared. We use actual Facebook data to show how our model can be applied to a real-world scenario.Peer ReviewedPostprint (published version