11 research outputs found
Ringo: Interactive Graph Analytics on Big-Memory Machines
We present Ringo, a system for analysis of large graphs. Graphs provide a way
to represent and analyze systems of interacting objects (people, proteins,
webpages) with edges between the objects denoting interactions (friendships,
physical interactions, links). Mining graphs provides valuable insights about
individual objects as well as the relationships among them.
In building Ringo, we take advantage of the fact that machines with large
memory and many cores are widely available and also relatively affordable. This
allows us to build an easy-to-use interactive high-performance graph analytics
system. Graphs also need to be built from input data, which often resides in
the form of relational tables. Thus, Ringo provides rich functionality for
manipulating raw input data tables into various kinds of graphs. Furthermore,
Ringo also provides over 200 graph analytics functions that can then be applied
to constructed graphs.
We show that a single big-memory machine provides a very attractive platform
for performing analytics on all but the largest graphs as it offers excellent
performance and ease of use as compared to alternative approaches. With Ringo,
we also demonstrate how to integrate graph analytics with an iterative process
of trial-and-error data exploration and rapid experimentation, common in data
mining workloads.Comment: 6 pages, 2 figure
Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue
Traditional recommendation systems produce static rather than interactive
recommendations invariant to a user's specific requests, clarifications, or
current mood, and can suffer from the cold-start problem if their tastes are
unknown. These issues can be alleviated by treating recommendation as an
interactive dialogue task instead, where an expert recommender can sequentially
ask about someone's preferences, react to their requests, and recommend more
appropriate items. In this work, we collect a goal-driven recommendation
dialogue dataset (GoRecDial), which consists of 9,125 dialogue games and 81,260
conversation turns between pairs of human workers recommending movies to each
other. The task is specifically designed as a cooperative game between two
players working towards a quantifiable common goal. We leverage the dataset to
develop an end-to-end dialogue system that can simultaneously converse and
recommend. Models are first trained to imitate the behavior of human players
without considering the task goal itself (supervised training). We then
finetune our models on simulated bot-bot conversations between two paired
pre-trained models (bot-play), in order to achieve the dialogue goal. Our
experiments show that models finetuned with bot-play learn improved dialogue
strategies, reach the dialogue goal more often when paired with a human, and
are rated as more consistent by humans compared to models trained without
bot-play. The dataset and code are publicly available through the ParlAI
framework.Comment: EMNLP 201