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Maryam Karimzadehgan

Maryam Karimzadehgan

Dr. Maryam Karimzadehgan is the Technical Lead of Android Intelligence responsible for delivering intelligent models on devices. She has served as the Program Committee of the SIGIR, WWW, CIKM, WSDM, ICTIR and ECIR conferences. She has published over 25 research papers. She has been a recipient of Google PhD fellowship and Yahoo! Key Scientific Challenges Award. She holds a PhD from University of Illinois at Urbana-Champaign.
Authored Publications
Google Publications
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    IMO^3: Interactive Multi-Objective Off-Policy Optimization
    Nan Wang
    Hongning Wang
    Branislav Kveton
    Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI-22), Vienna (2022), pp. 3523-3529 (to appear)
    Preview abstract Most real-world optimization problems have multiple objectives. A system designer needs to find a policy that trades off these objectives to reach a desired operating point. This problem has been studied extensively in the setting of known objective functions. However, we consider a more practical but challenging setting of unknown objective functions. In industry, optimization under this setting is mostly approached with online A/B testing, which is often costly and inefficient. As an alternative, we propose Interactive Multi-Objective Off-policy Optimization (IMO3). The key idea of IMO3 is to interact with a system designer using policies evaluated in an off-policy fashion to uncover which policy maximizes her unknown utility function. We theoretically show that IMO3 identifies a near-optimal policy with high probability, depending on the amount of designer feedback and training data for off-policy estimation. We demonstrate its effectiveness empirically on several multi-objective optimization problems. View details
    Separate And Attend in Personal Email Search
    Yu Meng
    Proceedings of the 13th ACM International Conference on Web Search and Data Mining (WSDM) (2020)
    Preview abstract In personal email search, user queries often impose different requirements on different aspects of the retrieved emails. For example, the query "my recent flight to the US'" requires emails to be ranked based on both textual contents and recency of the email documents, while other queries such as "medical history'" do not impose any constraints on the recency of the email. Recent deep learning-to-rank models for personal email search often directly concatenate dense numerical features with embedded sparse features (e.g, n-gram embeddings). In this paper, we first show with a set of experiments on synthetic datasets that direct concatenation of dense and sparse features does not lead to the optimal search performance of deep neural ranking models. To effectively incorporate both sparse and dense email features into personal email search ranking, we propose a novel neural model, sepattn. sepattn first builds two separate neural models to learn from sparse and dense features respectively, and then applies an attention mechanism at the prediction level to derive the final prediction from these two models. We conduct a comprehensive set of experiments on a large-scale email search dataset, and demonstrate that our sepattn model consistently improves the search quality over the baseline models. View details
    Domain Adaptation for Enterprise Email Search
    Brandon Tran
    Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (2019)
    Preview abstract In the enterprise email search setting, the same search engine often powers multiple enterprises from various industries: technology, education, manufacturing, etc. However, using the same global ranking model across different enterprises may result in suboptimal search quality, due to the corpora differences and distinct information needs. On the other hand, training an individual ranking model for each enterprise may be infeasible, especially for smaller institutions with limited data. To address this data challenge, in this paper we propose a domain adaptation approach that fine-tunes the global model to each individual enterprise. In particular, we propose a novel application of the Maximum Mean Discrepancy (MMD) approach to information retrieval, which attempts to bridge the gap between the global data distribution and the distribution arising from an individual enterprise. We conduct a comprehensive set of experiments on a large-scale email search engine, and demonstrate that the MMD approach consistently improves the search quality for multiple individual domains, both in comparison to the global ranking model, as well as several competitive domain adaptation baselines including adversarial learning methods. View details
    Multi-Task Learning for Personal Search Ranking with Query Clustering
    Jiaming Shen
    Proceedings of ACM Conference on Information and Knowledge Management (CIKM) (2018)
    Preview abstract User needs vary significantly across different tasks, and therefore their queries will also vary significantly in their expressiveness and semantics. Many studies have been proposed to model such query diversity by obtaining query types and building query-dependent ranking models. To obtain query types, these studies typically require either a labeled query dataset or clicks from multiple users aggregated over the same document. These techniques, however, are not applicable when manual query labeling is not viable, and aggregated clicks are unavailable due to the private nature of the document collection, e.g., in personal search scenarios. Therefore, in this paper, we study the problem of how to obtain query type in an unsupervised fashion and how to leverage this information using query-dependent ranking models in personal search. We first develop a hierarchical clustering algorithm based on truncated SVD and varimax rotation to obtain coarse-to-fine query types. Then, we propose three query-dependent ranking models, including two neural models that leverage query type information as additional features, and one novel multi-task neural model that is trained to simultaneously rank documents and predict query types. We evaluate our ranking models using the click data collected from one of the world’s largest personal search engines. The experiments demonstrate that the proposed multi-task model can significantly outperform the baseline neural models, which either do not incorporate query type information or just simply feed query type as an additional feature. To the best of our knowledge, this is the first successful application of query-dependent multi-task learning in personal search ranking. View details
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