Search

In this article, we address the problem of answering complex information needs by conducting conversations with search engines, in the sense that users can express their queries in natural language and directly receive the information they need from a short system response in a conversationalmanner. Recently, there have been some attempts towards a similar goal, e.g., studies on Conversational Agents (CAs) and Conversational Search (CS). However, they either do not address complex information needs in search scenarios or they are limited to the development of conceptual frameworks and/or laboratory-based user studies. We pursue two goals in this article: (1) the creation of a suitable dataset, the Search as a Conversation (SaaC) dataset, for the development of pipelines for conversations with search engines, and (2) the development of a state-of-the-art pipeline for conversations with search engines, Conversations with Search Engines (CaSE), using this dataset. SaaC is built based on a multi-turn conversational search dataset, where we further employ workers from a crowdsourcing platform to summarize each relevant passage into a short, conversational response. CaSE enhances the state-of-the-art by introducing a supporting token identification module and a prior-aware pointer generator, which enables us to generate more accurate responses. We carry out experiments to show that CaSE is able to outperform strong baselines. We also conduct extensive analyses on the SaaC dataset to show where there is room for further improvement beyond CaSE. Finally, we release the SaaC dataset and the code for CaSE and all models used for comparison to facilitate future research on this topic. [ABSTRACT FROM AUTHOR]

Academic search engines have been widely used to access academic papers, where users' information needs are explicitly represented as search queries. Some modern recommender systems have taken one step further by predicting users' information needswithout the presence of an explicit query. In this article,we examine an academic paper recommender that sends out paper recommendations in email newsletters, based on the users' browsing history on the academic search engine. Specifically, we look at users who regularly browse papers on the search engine, and we sign up for the recommendation newsletters for the first time. We address the task of reranking the recommendation candidates that are generated by a production system for such users. We face the challenge that the users on whom we focus have not interacted with the recommender system before, which is a common scenario that every recommender system encounters when new users sign up. We propose an approach to reranking candidate recommendations that utilizes both paper content and user behavior. The approach is designed to suit the characteristics unique to our academic recommendation setting. For instance, content similarity measures can be used to find the closest match between candidate recommendations and the papers previously browsed by the user. To this end, we use a knowledge graph derived from paper metadata to compare entity similarities (papers, authors, and journals) in the embedding space. Since the users on whom we focus have no prior interactions with the recommender system, we propose a model to learn a mapping from users' browsed articles to user clicks on the recommendations. We combine both content and behavior into a hybrid reranking model that outperforms the production baseline significantly, providing a relative 13% increase in Mean Average Precision and 28% in Precision@1. Moreover, we provide a detailed analysis of the model components, highlighting where the performance boost comes from. The obtained insights reveal useful components for the reranking process and can be generalized to other academic recommendation settings as well, such as the utility of graph embedding similarity. Also, recent papers browsed by users provide stronger evidence for recommendation than historical ones. [ABSTRACT FROM AUTHOR]

Ranker evaluation is central to the research into search engines, be it to compare rankers or to provide feedback for learning to rank. Traditional evaluation approaches do not scale well because they require explicit relevance judgments of document-query pairs, which are expensive to obtain. A promising alternative is the use of interleaved comparison methods, which compare rankers using click data obtained when interleaving their rankings. In this article, we propose a framework for analyzing interleaved comparison methods. An interleaved comparison method has fidelity if the expected outcome of ranker comparisons properly corresponds to the true relevance of the ranked documents. It is sound if its estimates of that expected outcome are unbiased and consistent. It is efficient if those estimates are accurate with only little data. We analyze existing interleaved comparison methods and find that, while sound, none meet our criteria for fidelity. We propose a probabilistic interleave method, which is sound and has fidelity. We show empirically that, by marginalizing out variables that are known, it is more efficient than existing interleaved comparison methods. Using importance sampling we derive a sound extension that is able to reuse historical data collected in previous comparisons of other ranker pairs. [ABSTRACT FROM AUTHOR]