Paper Session 7: Using Side-Information and User Attributes and Cold-Start in Recommender Algorithms

Date: Wednesday, Sept 18, 2019, 16:00-17:30
Location: Auditorium
Chair: Bamshad Mobasher

  • LPDeep Social Collaborative Filtering
    by Wenqi Fan, Yao Ma, Dawei Yin, Jianping Wang, Jiliang Tang, Qing Li

    Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users’ preference towards items via their interactions based on collaborative filtering techniques. In addition to the user-item interactions, social networks can also provide useful information to understand users’ preference as suggested by the social theories such as homophily and influence. Recently, deep neural networks have been utilized for social recommendations, which facilitate both the user-item interactions and the social network information. However, most of these models cannot take full advantage of the social network information. They only use information from direct neighbors, but distant neighbors can also provide helpful information. Meanwhile, most of these models treat neighbors’ information equally without considering the specific recommendations. However, for a specific recommendation case, the information relevant to the specific item would be helpful. Besides, most of these models do not explicitly capture the neighbor’s opinions to items for social recommendations, while different opinions could affect the user differently. In this paper, to address the aforementioned challenges, we propose DSCF, a Deep Social Collaborative Filtering framework, which can exploit the social relations with various aspects for recommender systems. Comprehensive experiments on two-real world datasets show the effectiveness of the proposed framework.

  • LPAttribute-Aware Non-Linear Co-Embeddings of Graph Features
    by Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme

    In very sparse recommender data sets, attributes of users such as age, gender and home location and attributes of items such as, in the case of movies, genre, release year, and director can improve the recommendation accuracy, especially for users and items that have few ratings. While most recommendation models can be extended to take attributes of users and items into account, their architectures usually become more complicated. While attributes for items are often easy to be provided, attributes for users are often scarce for reasons of privacy or simply because they are not relevant to the operational process at hand. In this paper, we address these two problems for attribute-aware recommender systems by proposing a simple model that co-embeds users and items into a joint latent space in a similar way as a vanilla matrix factorization, but with non-linear latent features construction that seamlessly can ingest user or item attributes or both (GraphRec). To address the second problem, scarce attributes, the proposed model treats the user-item relation as a bipartite graph and constructs generic user and item attributes via the Laplacian of the user-item co-occurrence graph that requires no further external side information but the mere rating matrix. In experiments on three recommender datasets, we show that GraphRec significantly outperforms existing state-of-the-art attribute-aware and content-aware recommender systems even without using any side information.

  • LPAdversarial Attacks on an Oblivious Recommender
    by Konstantina Christakopoulou, Arindam Banerjee

    Can machine learning models be easily fooled? Despite the recent surge of interest in learned adversarial attacks in other domains, in the context of recommendation systems this question has mainly been answered using hand-engineered fake user profiles. This paper attempts to reduce this gap. We provide a formulation for learning to attack a recommender as a repeated general-sum game between two players, i.e., an adversary and a recommender oblivious to the adversary’s existence. We consider the challenging case of poisoning attacks, which focus on the training phase of the recommender model. We generate adversarial user profiles targeting subsets of users or items, or generally the top-K recommendation quality. Moreover, we ensure that the adversarial user profiles remain unnoticeable by preserving proximity of the real user rating/ interaction distribution to the adversarial fake user distribution. To cope with the challenge of the adversary not having access to the gradient of the recommender’s objective with respect to the fake user profiles, we provide a non-trivial algorithm building upon zero-order optimization techniques. We offer a wide range of experiments, instantiating the proposed method for the case of the classic popular approach of a low-rank recommender, and illustrating the extent of the recommender’s vulnerability to a variety of adversarial intents. These results can serve as a motivating point for more research into recommender defense strategies against machine learned attacks.

  • LPHybridSVD: When Collaborative Information is Not Enough
    by Evgeny Frolov, Ivan Oseledets

    We propose a new hybrid algorithm that allows incorporating both user and item side information within the standard collaborative filtering technique. One of its key features is that it naturally extends a simple PureSVD approach and inherits its unique advantages, such as highly efficient Lanczos-based optimization procedure, simplified hyper-parameter tuning and a quick folding-in computation for generating recommendations instantly even in highly dynamic online environments. The algorithm utilizes a generalized formulation of the singular value decomposition, which adds flexibility to the solution and allows imposing the desired structure on its latent space. Conveniently, the resulting model also admits an efficient and straightforward solution for the cold start scenario. We evaluate our approach on a diverse set of datasets and show its superiority over similar classes of hybrid models.

  • LPVariational Low Rank Multinomials for Collaborative Filtering with Side-Information
    by Ehtsham Elahi, Wei Wang, Dave Ray, Aish Fenton, Tony Jebara

    We are interested in Bayesian models for collaborative filtering that incorporate side-information or metadata about items in addition to user-item interaction data. We present a simple and flexible framework to build models for this task that exploit the low-rank structure in user-item interaction datasets. Although the resulting models are non-conjugate, we develop an efficient technique for approximating posteriors over model parameters using variational inference. We borrow the ‘re-parameterization trick’ from Bayesian deep learning literature to enable variational inference in our models. The resulting approximate Bayesian inference algorithm is scalable and can handle large scale datasets. We demonstrate our ideas on three real world datasets where we show competitive performance against widely used baselines

  • SPOQuick and Accurate Attack Detection in Recommender Systems through User Attributes
    by Mehmet Aktukmak, Yasin Yilmaz, Ismail Uysal

    Malicious profiles have been a credible threat to collaborative recommender systems. Attackers provide fake item ratings to systematically manipulate the platform. Attack detection algorithms can identify and remove such users by observing rating distributions. In this study, we aim to use the user attributes as an additional information source to improve the accuracy and speed of attack detection. We propose a probabilistic factorization model which can embed mixed data type user attributes and observed ratings into a latent space to generate anomaly statistics for new users. To identify the persistent outliers in the system, we also propose a sequential attack detection algorithm to enable quick and accurate detection based on the probabilistic model learned from genuine users. The proposed model demonstrates significant improvements in both accuracy and speed when compared to baseline algorithms on a popular benchmark dataset.

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