Maarten de Rijke

Information retrieval

Category: Publications (page 1 of 7)

RecSys 2018 paper on preference elicitation as an optimization problem online

The following RecSys 2018 paper on preference elicitation as an optimization problem is online now:

  • Anna Sepliarskaia, Julia Kiseleva, Filip Radlinski, and Maarten de Rijke. Preference elicitation as an optimization problem. In RecSys 2018: The ACM Conference on Recommender Systems. ACM, October 2018. Bibtex, PDF
    @inproceedings{sepliarskaia-preference-2018,
    Author = {Sepliarskaia, Anna and Kiseleva, Julia and Radlinski, Filip and de Rijke, Maarten},
    Booktitle = {RecSys 2018: The ACM Conference on Recommender Systems},
    Date-Added = {2018-07-10 09:40:05 +0000},
    Date-Modified = {2018-08-07 05:45:23 +0200},
    Month = {October},
    Publisher = {ACM},
    Title = {Preference elicitation as an optimization problem},
    Year = {2018}}

The new user coldstart problem arises when a recommender system does not yet have any information about a user. A common solution to it is to generate a profile by asking the user to rate a number of items. Which items are selected determines the quality of the recommendations made, and thus has been studied extensively. We propose a new elicitation method to generate a static preference questionnaire (SPQ) that poses relative preference questions to the user. Using a latent factor model, we show that SPQ improves personalized recommendations by choosing a minimal and diverse set of questions. We are the first to rigorously prove which optimization task should be solved to select each question in static questionnaires. Our theoretical results are confirmed by extensive experimentation. We test the performance of SPQ on two real-world datasets, under two experimental conditions: simulated, when users behave according to a latent factor model (LFM), and real, in which only real user judgments are revealed as the system asks questions. We show that SPQ reduces the necessary length of a questionnaire by up to a factor of three compared to state-of-the-art preference elicitation methods. Moreover, solving the right optimization task, SPQ also performs better than baselines with dynamically generated questions.

ISWC 2018 paper on measuring semantic coherence of a conversation online

The following ISWC 2018 paper on measuring semantic coherence of a conversation is online now:

  • Svitlana Vakulenko, Maarten de Rijke, Michael Cochez, Vadim Savenkov, and Axel Polleres. Measuring semantic coherence of a conversation. In ISWC 2018: 17th International Semantic Web Conference. Springer, October 2018. Bibtex, PDF
    @inproceedings{vakulenko-measuring-2018,
    Author = {Vakulenko, Svitlana and de Rijke, Maarten and Cochez, Michael and Savenkov, Vadim and Polleres, Axel},
    Booktitle = {ISWC 2018: 17th International Semantic Web Conference},
    Date-Added = {2018-05-26 04:41:16 +0000},
    Date-Modified = {2018-08-07 05:45:32 +0200},
    Month = {October},
    Publisher = {Springer},
    Title = {Measuring semantic coherence of a conversation},
    Year = {2018}}

Conversational systems have become increasingly popular as a way for people to interact with computers. To be able to provide intelligent responses, conversational systems must correctly model the structure and semantics of a conversation. We introduce the task of measuring semantic (in)coherence in a conversation with respect to background knowledge, which relies on the identification of semantic relations between concepts introduced during a conversation. We propose and evaluate graph-based and machine learning-based approaches for measuring semantic coherence using knowledge graphs, their vector space embeddings and word embedding models, as sources of background knowledge. We demonstrate how these approaches are able to uncover different coherence patterns in conversations on the Ubuntu Dialogue Corpus.

Now on arXiv: Explainable Fashion Recommendation with Joint Outfit Matching and Comment Generation

Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, and I published “Explainable Fashion Recommendation with Joint Outfit Matching and Comment Generation” on arXiv. Most previous work on fashion recommendation focuses on designing visual features to enhance recommendations. Existing work neglects user comments of fashion items, which have been proved effective in generating explanations along with better recommendation results. We propose a novel neural network framework, neural fashion recommendation (NFR), that simultaneously provides fashion recommendations and generates abstractive comments. NFR consists of two parts: outfit matching and comment generation. For outfit matching, we propose a convolutional neural network with a mutual attention mechanism to extract visual features of outfits. The visual features are then decoded into a rating score for the matching prediction. For abstractive comment generation, we propose a gated recurrent neural network with a cross-modality attention mechanism to transform visual features into a concise sentence. The two parts are jointly trained based on a multi-task learning framework in an end-to-end back-propagation paradigm. Extensive experiments conducted on an existing dataset and a collected real-world dataset show NFR achieves significant improvements over state-of-the-art baselines for fashion recommendation. Meanwhile, our generated comments achieve impressive ROUGE and BLEU scores in comparison to human-written comments. The generated comments can be regarded as explanations for the recommendation results. We release the dataset and code to facilitate future research. You can find the paper here.

Three more papers on arXiv

We’ve just put three more papers on arXiv.

Earlier in June, Sapna Negi, Paul Buitelaar, and I put “Open Domain Suggestion Mining: Problem Definition and Datasets” on arXiv. In the paper we propose a formal definition for the task of suggestion mining in the context of a wide range of open domain applications. Human perception of the term suggestion is subjective and this effects the preparation of hand labeled datasets for the task of suggestion mining. Existing work either lacks a formal problem definition and annotation procedure, or provides domain and application specific definitions. Moreover, many previously used manually labeled datasets remain proprietary. We first present an annotation study, and based on our observations propose a formal task definition and annotation procedure for creating benchmark datasets for suggestion mining. With this study, we also provide publicly available labeled datasets for suggestion mining in multiple domains. You can find the paper here.

Then, in mid June, Branislav Kveton, Chang Li, Tor Lattimore, Ilya Markov, Csaba Szepesvari, Masrour Zoghi, and I put “BubbleRank: Safe Online Learning to Rerank” on arXiv. We study the problem of online learning to re-rank, where users provide feedback to improve the quality of displayed lists. Learning to rank has been traditionally studied in two settings. In the offline setting, rankers are typically learned from relevance labels of judges. These approaches have become the industry standard. However, they lack exploration, and thus are limited by the information content of offline data. In the online setting, an algorithm can propose a list and learn from the feedback on it in a sequential fashion. Bandit algorithms developed for this setting actively experiment, and in this way overcome the biases of offline data. But they also tend to ignore offline data, which results in a high initial cost of exploration. We propose BubbleRank, a bandit algorithm for re-ranking that combines the strengths of both settings. The algorithm starts with an initial base list and improves it gradually by swapping higher-ranked less attractive items for lower-ranked more attractive items. We prove an upper bound on the n-step regret of BubbleRank that degrades gracefully with the quality of the initial base list. Our theoretical findings are supported by extensive numerical experiments on a large real-world click dataset. The paper can be found here.

And, third, Svitlana Vakulenko, Michael Cochez, Vadim Savenkov, Axel Polleres, and I put “Measuring Semantic Coherence of a Conversation” on arXiv. Conversational systems have become increasingly popular as a way for humans to interact with computers. To be able to provide intelligent responses, conversational systems must correctly model the structure and semantics of a conversation. We introduce the task of measuring semantic (in)coherence in a conversation with respect to background knowledge, which relies on the identification of semantic relations between concepts introduced during a conversation. We propose and evaluate graph-based and machine learning-based approaches for measuring semantic coherence using knowledge graphs, their vector space embeddings and word embedding models, as sources of background knowledge. We demonstrate how these approaches are able to uncover different coherence patterns in conversations on the Ubuntu Dialogue Corpus. The paper can be found here.

SIGIR 2018 papers online

The SIGIR 2018 papers that I contributed to are online now:

  • Alexey Borisov, Martijn Wardenaar, Ilya Markov, and Maarten de Rijke. A click sequence model for web search. In SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2018. Bibtex, PDF
    @inproceedings{borisov-click-2018,
    Author = {Borisov, Alexey and Wardenaar, Martijn and Markov, Ilya and de Rijke, Maarten},
    Booktitle = {SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval},
    Date-Added = {2018-04-12 05:45:13 +0000},
    Date-Modified = {2018-08-07 05:46:05 +0200},
    Month = {July},
    Publisher = {ACM},
    Title = {A click sequence model for web search},
    Year = {2018}}
  • Wanyu Chen, Fei Cai, Honghui Chen, and Maarten de Rijke. Attention-based hierarchical neural query suggestion. In SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2018. Bibtex, PDF
    @inproceedings{chen-attention-based-2018,
    Author = {Chen, Wanyu and Cai, Fei and Chen, Honghui and de Rijke, Maarten},
    Booktitle = {SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval},
    Date-Added = {2018-04-11 23:31:34 +0000},
    Date-Modified = {2018-08-07 05:46:18 +0200},
    Month = {July},
    Publisher = {ACM},
    Title = {Attention-based hierarchical neural query suggestion},
    Year = {2018}}
  • Paul Groth, Laura Koesten, Philipp Mayr, Maarten de Rijke, and Elena Simperl. DATA:SEARCH’18 – Searching data on the web. In SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2018. Bibtex, PDF
    @inproceedings{groth-data-2018,
    Author = {Groth, Paul and Koesten, Laura and Mayr, Philipp and de Rijke, Maarten and Simperl, Elena},
    Booktitle = {SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval},
    Date-Added = {2018-05-05 10:58:53 +0000},
    Date-Modified = {2018-08-07 06:12:44 +0200},
    Publisher = {ACM},
    Title = {DATA:SEARCH'18 -- Searching data on the web},
    Year = {2018}}
  • Harrie Oosterhuis and Maarten de Rijke. Ranking for relevance and display preferences in complex presentation layouts. In SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2018. Bibtex, PDF
    @inproceedings{oosterhuis-ranking-2018,
    Author = {Oosterhuis, Harrie and de Rijke, Maarten},
    Booktitle = {SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval},
    Date-Added = {2018-04-12 05:41:46 +0000},
    Date-Modified = {2018-08-07 05:46:31 +0200},
    Month = {July},
    Publisher = {ACM},
    Title = {Ranking for relevance and display preferences in complex presentation layouts},
    Year = {2018}}
  • Zhaochun Ren, Xiangnan He, Dawei Yin, and Maarten de Rijke. Information discovery in e-commerce. In SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2018. Bibtex, PDF
    @inproceedings{ren-information-2018,
    Author = {Ren, Zhaochun and He, Xiangnan and Yin, Dawei and de Rijke, Maarten},
    Booktitle = {SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval},
    Date-Added = {2018-05-05 10:53:55 +0000},
    Date-Modified = {2018-08-07 06:12:35 +0200},
    Publisher = {ACM},
    Title = {Information discovery in e-commerce},
    Year = {2018}}
  • Christophe Van Gysel and Maarten de Rijke. Pytrec_eval: An extremely fast Python interface to trec_eval. In SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2018. Bibtex, PDF
    @inproceedings{vangysel-pytrec-2018,
    Author = {Van Gysel, Christophe and de Rijke, Maarten},
    Booktitle = {SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval},
    Date-Added = {2018-04-11 22:02:31 +0000},
    Date-Modified = {2018-08-07 05:46:49 +0200},
    Month = {July},
    Publisher = {ACM},
    Title = {Pytrec\_eval: An extremely fast Python interface to trec\_eval},
    Year = {2018}}
  • Nikos Voskarides, Edgar Meij, Ridho Reinanda, Abhinav Khaitan, Miles Osborne, Giorgio Stefanoni, Kambadur Prabhanjan, and Maarten de Rijke. Weakly-supervised contextualization of knowledge graph facts. In SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2018. Bibtex, PDF
    @inproceedings{voskarides-weakly-supervised-2018,
    Author = {Voskarides, Nikos and Meij, Edgar and Reinanda, Ridho and Khaitan, Abhinav and Osborne, Miles and Stefanoni, Giorgio and Kambadur Prabhanjan and de Rijke, Maarten},
    Booktitle = {SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval},
    Date-Added = {2018-04-12 05:42:50 +0000},
    Date-Modified = {2018-08-07 05:47:00 +0200},
    Month = {July},
    Publisher = {ACM},
    Title = {Weakly-supervised contextualization of knowledge graph facts},
    Year = {2018}}
  • Xiaohui Xie, Jiaxin Mao, Maarten de Rijke, Ruizhe Zhang, Min Zhang, and Shaoping Ma. Constructing an interaction behavior model for web image search. In SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2018. Bibtex, PDF
    @inproceedings{xie-constructing-2018,
    Author = {Xie, Xiaohui and Mao, Jiaxin and de Rijke, Maarten and Zhang, Ruizhe and Zhang, Min and Ma, Shaoping},
    Booktitle = {SIGIR 2018: 41st international ACM SIGIR conference on Research and Development in Information Retrieval},
    Date-Added = {2018-04-11 22:25:25 +0000},
    Date-Modified = {2018-08-07 05:47:16 +0200},
    Month = {July},
    Publisher = {ACM},
    Title = {Constructing an interaction behavior model for web image search},
    Year = {2018}}

Now on arXiv: Finding influential training samples for gradient boosted decision trees

Boris Sharchilev, Yury Ustinovsky, Pavel Serdyukov, and I have released a new pre-print on “finding influential training samples for gradient boosted decision trees” on arXiv. In the paper we address the problem of finding influential training samples for a particular case of tree ensemble-based models, e.g., Random Forest (RF) or Gradient Boosted Decision Trees (GBDT). A natural way of formalizing this problem is studying how the model’s predictions change upon leave-one-out retraining, leaving out each individual training sample. Recent work has shown that, for parametric models, this analysis can be conducted in a computationally efficient way. We propose several ways of extending this framework to non-parametric GBDT ensembles under the assumption that tree structures remain fixed. Furthermore, we introduce a general scheme of obtaining further approximations to our method that balance the trade-off between performance and computational complexity. We evaluate our approaches on various experimental setups and use-case scenarios and demonstrate both the quality of our approach to finding influential training samples in comparison to the baselines and its computational efficiency. You can find the paper here.

Now on arXiv: Optimizing interactive systems with data-driven objectives

Ziming Li, Artem Grotov, Julia Kiseleva, Harrie Oosterhuis and I have just released a new preprint on “optimizing interactive systems with data-driven objectives” on arXiv. Effective optimization is essential for interactive systems to provide a satisfactory user experience. However, it is often challenging to find an objective to optimize for. Generally, such objectives are manually crafted and rarely capture complex user needs accurately. Conversely, we propose an approach that infers the objective directly from observed user interactions. These inferences can be made regardless of prior knowledge and across different types of user behavior. Then we introduce: Interactive System Optimizer (ISO), a novel algorithm that uses these inferred objectives for optimization. Our main contribution is a new general principled approach to optimizing interactive systems using data-driven objectives. We demonstrate the high effectiveness of ISO over several GridWorld simulations. Rush over to arXiv to download the paper.

ICLR 2018 paper on Deep Learning with Logged Bandit Feedback online now

“Deep Learning with Logged Bandit Feedback” by Thorsten Joachims, Adith Swaminathan and Maarten de Rijke, to be published at ICLR 2018, is available online.

In the paper we propose a new output layer for deep neural networks that permits the use of logged contextual bandit feedback for training. Such contextual bandit feedback can be available in huge quantities (e.g., logs of search engines, recommender systems) at little cost, opening up a path for training deep networks on orders of magnitude more data. To this effect, we propose a counterfactual risk minimization approach for training deep networks using an equivariant empirical risk estimator with variance regularization, BanditNet, and show how the resulting objective can be decomposed in a way that allows stochastic gradient descent training. We empirically demonstrate the effectiveness of the method by showing how deep networks – ResNets in particular – can be trained for object recognition without conventionally labeled images.

WWW 2018 paper on Manifold Learning for Rank Aggregation online

“Manifold Learning for Rank Aggregation” by Shangsong Liang, Ilya Markov, Zhaochun Ren, and Maarten de Rijke, which will be published at WWW 2018, is available online now.

In the paper we address the task of fusing ranked lists of documents that are retrieved in response to a query. Past work on this task of rank aggregation often assumes that documents in the lists being fused are independent and that only the documents that are ranked high in many lists are likely to be relevant to a given topic. We propose manifold learning aggregation approaches, ManX and v-ManX, that build on the cluster hypothesis and exploit inter-document similarity information. ManX regularizes document fusion scores, so that documents that appear to be similar within a manifold, receive similar scores, whereas v-ManX first generates virtual adversarial documents and then regularizes the fusion scores of both original and virtual adversarial documents. Since aggregation methods built on the cluster hypothesis are computationally expensive, we adopt an optimization method that uses the top-k documents as anchors and considerably reduces the computational complexity of manifold-based methods, resulting in two efficient aggregation approaches, a-ManX and a-v-ManX. We assess the proposed approaches experimentally and show that they signi cantly outperform the state-of-the-art aggregation approaches, while a-ManX and a-v-ManX run faster than ManX, v-ManX, respectively.

JASIST paper “The birth of collective memories: Analyzing emerging entities in text streams” online

“The birth of collective memories: Analyzing emerging entities in text streams” by David Graus, Daan Odijk and Maarten de Rijke, to be published in the Journal of the Association for Information Science and Technology is online now at this location.

In the paper we study how collective memories are formed online. We do so by tracking entities that emerge in public discourse, that is, in online text streams such as social media and news streams, before they are incorporated into Wikipedia, which, we argue, can be viewed as an online place for collective memory. By tracking how entities emerge in public discourse, that is, the temporal patterns between their first mention in online text streams and subsequent incorporation into collective memory, we gain insights into how the collective remembrance process happens online. Specifically, we analyze nearly 80,000 entities as they emerge in online text streams before they are incorporated into Wikipedia. The online text streams we use for our analysis comprise of social media and news streams, and span over 579 million documents in a time span of 18 months. We discover two main emergence patterns: entities that emerge in a “bursty” fashion, that is, that appear in public discourse without a precedent, blast into activity and transition into collective memory. Other entities display a “delayed” pattern, where they appear in public discourse, experience a period of inactivity, and then resurface before transitioning into our cultural collective memory.

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