Sequential Recommendation with Self-Attentive Multi-Adversarial Network

Recently, deep learning has made significant progress in the task of sequential recommendation. Existing neural sequential recommenders typically adopt a generative way trained with Maximum Likelihood Estimation (MLE). When context information (called factor) is involved, it is difficult to analyze when and how each individual factor would affect the final recommendation performance. For this purpose, we take a new perspective and introduce adversarial learning to sequential recommendation. In this paper, we present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation. Specifically, our proposed MFGAN has two kinds of modules: a Transformer-based generator taking user behavior sequences as input to recommend the possible next items, and multiple factor-specific discriminators to evaluate the generated sub-sequence from the perspectives of different factors. To learn the parameters, we adopt the classic policy gradient method, and utilize the reward signal of discriminators for guiding the learning of the generator. Our framework is flexible to incorporate multiple kinds of factor information, and is able to trace how each factor contributes to the recommendation decision over time. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed model over the state-of-the-art methods, in terms of effectiveness and interpretability

Learning to Attend, Copy, and Generate for Session-Based Query Suggestion

Users try to articulate their complex information needs during search sessions by reformulating their queries. To make this process more effective, search engines provide related queries to help users in specifying the information need in their search process. In this paper, we propose a customized sequence-to-sequence model for session-based query suggestion. In our model, we employ a query-aware attention mechanism to capture the structure of the session context. is enables us to control the scope of the session from which we infer the suggested next query, which helps not only handle the noisy data but also automatically detect session boundaries. Furthermore, we observe that, based on the user query reformulation behavior, within a single session a large portion of query terms is retained from the previously submitted queries and consists of mostly infrequent or unseen terms that are usually not included in the vocabulary. We therefore empower the decoder of our model to access the source words from the session context during decoding by incorporating a copy mechanism. Moreover, we propose evaluation metrics to assess the quality of the generative models for query suggestion. We conduct an extensive set of experiments and analysis. e results suggest that our model outperforms the baselines both in terms of the generating queries and scoring candidate queries for the task of query suggestion.Comment: Accepted to be published at The 26th ACM International Conference on Information and Knowledge Management (CIKM2017

Signed Distance-based Deep Memory Recommender

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e.g., matrix factorization) assume a linear relationship between the user and item. This approach limits the capacity of recommender systems, since the interactions between users and items in real-world applications are much more complex than the linear relationship. To overcome this limitation, in this paper, we design and propose a deep learning framework called Signed Distance-based Deep Memory Recommender, which captures non-linear relationships between users and items explicitly and implicitly, and work well in both general recommendation task and shopping basket-based recommendation task. Through an extensive empirical study on six real-world datasets in the two recommendation tasks, our proposed approach achieved significant improvement over ten state-of-the-art recommendation models

Culture and disaster risk management - stakeholder attitudes during Stakeholder Assembly in Lisbon, Portugal

This report provides a summary of the topics discussed and the results of the third CARISMAND Stakeholder Assembly conducted in Lisbon, Portugal on 27-28 February 2018. In order to promote cross-sectional knowledge transfer and gather a variety of attitudes and perceptions, as in the first and second CARISMAND Stakeholder Assemblies held in Romania and Italy in the previous years, the audience consisted of a wide range of practitioners who are typically involved in disaster management, e.g., civil protection, the emergency services, paramedics, nurses, environmental protection, Red Cross, firefighters, military, and the police. Further, these practitioners were from several regions in Portugal, including the island of Madeira. The 40 participants were recruited via invitations sent to various Portuguese organisations and institutions, and via direct contacts of the Civil Protection Department in Lisbon which is one of the partners in the CARISMAND consortium. The event consisted of a mix of presentations and discussion groups to combine dissemination with information gathering (for the detailed schedule/programme see Appendix 1). Furthermore, this third Stakeholder Assembly was organised and specifically designed to discuss and collect feedback on a comprehensive set of recommendations for disaster practitioners, which will form one of the core elements of the CARISMAND Work Package 9 ‘Toolkit’. These recommendations, which have all been formulated on the basis of Work Packages 2-10 results, were structured in four, main “sets”: 1. Approaches to ethnicity in disaster management; 2. Culturally aware disaster-related training activities; 3. Cultural factors in disaster communication, with the sub-sets: a. Cultural values and emotions; (cross-)cultural symbols; “physical” aides and methods; b. Involvement of cultural leaders; involvement of specific groups; usage of social media and mobile phone apps; and 4. Improving trust, improving disaster management. In an initial general assembly, the event started with presentations of the CARISMAND project and its main goals and concepts, including the concept of culture adopted by CARISMAND, and the planned CARISMAND Toolkit architecture and functionalities. These were followed by a detailed presentation of the first of the above mentioned sets of recommendations for practitioners. Then, participants of the Stakeholder Assembly were split into small groups in separate breakout rooms, where they discussed and provided feedback to the presented recommendations. Over the course of the 2-day event, this procedure was followed for all four sets of recommendations. To follow the cyclical design of CARISMAND events, and wherever meaningful and possible, the respective Toolkit recommendations for practitioners provided also the basis for a respective “shadow” recommendation for citizens which will be discussed accordingly in the last round of CARISMAND Citizen Summits (Citizen Summit 5 in Lisbon, and Citizen Summit 6 in Utrecht) in 2018. The location of the Third Stakeholder Assembly was selected to make use of the extensive local professional network of the Civil Protection Department in Lisbon, but also due to Portugal being a traditional “melting pot” where, over more than a millennium, people from different cultural backgrounds and local/ethnical origins (in particular Africa, South America, and Europe) have lived both alongside and together. All documents related to the Working Groups, i.e. discussion guidelines and consent forms, were translated into Portuguese. Accordingly, all presentations, as well as the group discussions were held in Portuguese, aiming to avoid any language/education-related access restrictions, and allowing participating practitioners to respond intuitively and discuss freely in their native language. For this purpose, simultaneous interpreters and professional local moderators were contracted via a local market research agency (EquaçãoLógica), which also provided the basic data analysis of all Working Group discussions and an independent qualitative evaluation of all recommendations presented in the event. The results of this analysis and evaluation will demonstrate that most recommendations were seen by the participating practitioners to be relevant and useful. In particular, those recommendations related to the use of cultural symbols and the potential of mobile phone apps and/or social media were perceived as stimulating and thought-provoking. Some recommendations were felt to be less relevant in the specific Portuguese context, but accepted as useful in other locations; a very small number was perceived to be better addressed to policy makers rather than practitioners. These and all other suggestions for improvement of the presented CARISMAND Toolkit recommendations for practitioners have been taken up and will be outlined in the final chapter of this report.The project was co-funded by the European Commission within the Horizon2020 Programme (2014-2020).peer-reviewe