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Overview of the SBS 2016 Interactive Track
Users looking for books online are confronted with both professional meta-data and user-generated content. The goal of the Interactive Social Book Search Track was to investigate how users used these two sources of information, when looking for books in a leisure context. To this end, participants recruited by seven teams performed two main tasks and one optional task using a user-interface that supports multiple search stages
Towards Question-based Recommender Systems
Conversational and question-based recommender systems have gained increasing
attention in recent years, with users enabled to converse with the system and
better control recommendations. Nevertheless, research in the field is still
limited, compared to traditional recommender systems. In this work, we propose
a novel Question-based recommendation method, Qrec, to assist users to find
items interactively, by answering automatically constructed and algorithmically
chosen questions. Previous conversational recommender systems ask users to
express their preferences over items or item facets. Our model, instead, asks
users to express their preferences over descriptive item features. The model is
first trained offline by a novel matrix factorization algorithm, and then
iteratively updates the user and item latent factors online by a closed-form
solution based on the user answers. Meanwhile, our model infers the underlying
user belief and preferences over items to learn an optimal question-asking
strategy by using Generalized Binary Search, so as to ask a sequence of
questions to the user. Our experimental results demonstrate that our proposed
matrix factorization model outperforms the traditional Probabilistic Matrix
Factorization model. Further, our proposed Qrec model can greatly improve the
performance of state-of-the-art baselines, and it is also effective in the case
of cold-start user and item recommendations.Comment: accepted by SIGIR 202
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Lessons learned from the CHiC and SBS interactive tracks: a wishlist for interactive IR evaluation
Over the course of the past two decades, the Interactive Tracks at TREC and INEX have contributed greatly to our knowledge of how to run an interactive IR evaluation campaign. In this position paper, we add to this body of knowledge by taking stock of our own experiences and challenges in organizing the CHIC and SBS Interactive Tracks from 2013 to 2016 in the form of a list of important properties of any future IIR evaluation campaigns
Overview of the CLEF 2016 Social Book Search Lab
The Social Book Search (SBS) Lab investigates book search in scenarios where users search with more than just a query, and look for more than objective metadata. Real-world information needs are generally complex, yet almost all research focuses instead on either relatively simple search based on queries, or on profile-based recommendation. The goal is to research and develop techniques to support users in complex book search tasks. The SBS Lab has three tracks. The aim of the Suggestion Track is to develop test collections for evaluating ranking effectiveness of book retrieval and recommender systems. The aim of the Interactive Track is to develop user interfaces that support users through each stage during complex search tasks and to investigate how users exploit professional metadata and user-generated content. The Mining Track focuses on detecting and linking book titles in online book discussion forums, as well as detecting book search research in forum posts for automatic book recommendation.Peer Reviewe
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