Accurate and Trustworthy Recommender Systems: Algorithms and Findings
Author(s)
Oh, Sejoon
Advisor(s)
Kumar, Srijan
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Abstract
The exponential growth of information on the Web has led to the problem of ”information overload,” which has been addressed through the use of recommender systems. Modern recommender systems use deep learning algorithms trained with user-item interaction data to generate recommendations. However, current recommender systems still face diverse challenges with respect to accuracy, personalization, and robustness. In this thesis, we investigate such challenges and provide insights and solutions to them. This thesis is divided into two parts: (1) making recommender systems accurate and personalized, and (2) making recommender systems robust and trustworthy.
First, we study session-based recommender systems (SBRSs) and user intent-aware recommender systems, which have been proposed to enhance accuracy and personalization via modeling users’ short-term and evolving interests. Existing recommender systems face two significant limitations. First, they cannot incorporate session contexts or user intents (i.e., high-level interests) into their models, which could improve the next-item prediction. To address it, we propose a novel SBRS: ISCON to assign precise and meaningful implicit contexts to sessions via node embedding and clustering algorithms. By leveraging the session contexts found by ISCON, we can offer more personalized recommendations to end users. We also propose a new recommendation framework: INTENTREC that predicts a user’s intent on Netflix and uses that as one of the input features of the next-item prediction of the user. The user intents obtained by INTENTREC can be used for diverse applications such as real-time recommendations, personalized UI and notifications, etc.
Second, existing recommender systems cannot scale to large real-world recommendation datasets. To handle the scalability issue, we propose M2TREC, a metadata-aware multi-task Transformer model that uses only item attributes to learn item representations and is completely item-ID free. With M2TREC, we can achieve faster convergence, higher accuracy, and robust recommendations with fewer training data.
Sparse training data can cause recommendation models to produce incorrect and popularity-biased recommendations. It has been well-known that most recommendation datasets are extremely large and sparse, limiting the ability of models to generate effective representations for cold-start users or items with few interactions. To address the sparsity issue, we devise an influence-guided data augmentation technique DAIN that augments important data points for reducing training loss to the original data. With DAIN, we can enhance the recommendation model’s generalization ability and mitigate cold-start and popularity-bias problems.
Apart from accuracy and personalization, we also analyze the robustness of existing recommender systems against input perturbations and devise a solution to enhance the robustness of the recommenders. Deep learning-based recommender systems have shown sensitivity to arbitrary and adversarial input perturbations, resulting in drastic alterations of recommendation lists after perturbations. The sensitivity disproportionately affects low-accuracy user groups compared to high-accuracy groups, making the models unreliable and detrimental to both users and service providers, particularly in high-stakes applications such as healthcare, education, and housing.
Despite its importance, the stability of recommender systems has not been studied thoroughly. Thus, we first introduce two Rank List Sensitivity (RLS) metrics that allow us to measure changes in recommendations against perturbations, and we propose two training data perturbation mechanisms (random and CASPER) for recommender systems. We show that existing sequential recommenders are highly vulnerable against CASPER and even random perturbations. We further introduce a fine-tuning mechanism called FINEST that can stabilize predictions of sequential recommender systems against training data perturbations. FINEST simulates perturbations during the fine-tuning and utilizes a rank-preserving loss function to ensure stable recommendations. With FINEST, any sequential recommenders become more robust against interaction-level perturbations.
Finally, we investigate the robustness of text-aware recommender systems against adversarial text rewriting. Our proposed text rewriting framework (ATR) can generate optimal product descriptions via two-phase fine-tuning of language models. Such rewritten product descriptions can significantly boost the ranks of target items, and the attackers can exploit the vulnerability of text-aware recommenders to promote their own items on diverse web platforms such as e-commerce. Our work highlights the importance of studying the robustness of existing recommenders and the need for inventing a defense mechanism against the text rewriting attack: ATR.
Overall, we proposed next-generation recommendation frameworks as per accuracy, personalization, and robustness. We also suggest several ongoing and future works including a unified robustness benchmark of existing recommender systems, adversarial attacks/defenses against multimodal recommenders, and leveraging emerging large language models to maximize the accuracy, personalization, and interpretability of recommender systems.
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Date
2024-04-15
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Text
Resource Subtype
Dissertation