2,829 research outputs found
Collaborative video searching on a tabletop
Almost all system and application design for multimedia systems is based around a single user working in isolation to perform some task yet much of the work for which we use computers to help us, is based on working collaboratively with colleagues. Groupware systems do support user collaboration but typically this is supported through software and users still physically work independently. Tabletop systems, such as the DiamondTouch from MERL, are interface devices which support direct user collaboration on a tabletop. When a tabletop is used as the interface for a multimedia system, such as a video search system, then this kind of direct collaboration raises many questions for system design. In this paper we present a tabletop system for supporting a pair of users in a video search task and we evaluate the system not only in terms of search performance but also in terms of userâuser interaction and how different user personalities within each pair of searchers impacts search performance and user interaction. Incorporating the user into the system evaluation as we have done here reveals several interesting results and has important ramifications for the design of a multimedia search system
JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions
Recently, personalized product search attracts great attention and many
models have been proposed. To evaluate the effectiveness of these models,
previous studies mainly utilize the simulated Amazon recommendation dataset,
which contains automatically generated queries and excludes cold users and tail
products. We argue that evaluating with such a dataset may yield unreliable
results and conclusions, and deviate from real user satisfaction. To overcome
these problems, in this paper, we release a personalized product search dataset
comprised of real user queries and diverse user-product interaction types
(clicking, adding to cart, following, and purchasing) collected from JD.com, a
popular Chinese online shopping platform. More specifically, we sample about
170,000 active users on a specific date, then record all their interacted
products and issued queries in one year, without removing any tail users and
products. This finally results in roughly 12,000,000 products, 9,400,000 real
searches, and 26,000,000 user-product interactions. We study the
characteristics of this dataset from various perspectives and evaluate
representative personalization models to verify its feasibility. The dataset
can be publicly accessed at Github: https://github.com/rucliujn/JDsearch.Comment: Accepted to SIGIR 202
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