510 research outputs found
Towards a Model of Understanding Social Search
Search engine researchers typically depict search as the solitary activity of
an individual searcher. In contrast, results from our critical-incident survey
of 150 users on Amazon's Mechanical Turk service suggest that social
interactions play an important role throughout the search process. Our main
contribution is that we have integrated models from previous work in
sensemaking and information seeking behavior to present a canonical social
model of user activities before, during, and after search, suggesting where in
the search process even implicitly shared information may be valuable to
individual searchers.Comment: Presented at 1st Intl Workshop on Collaborative Information Seeking,
2008 (arXiv:0908.0583
Hierarchical Reinforcement Learning for Modeling User Novelty-Seeking Intent in Recommender Systems
Recommending novel content, which expands user horizons by introducing them
to new interests, has been shown to improve users' long-term experience on
recommendation platforms \cite{chen2021values}. Users however are not
constantly looking to explore novel content. It is therefore crucial to
understand their novelty-seeking intent and adjust the recommendation policy
accordingly. Most existing literature models a user's propensity to choose
novel content or to prefer a more diverse set of recommendations at individual
interactions. Hierarchical structure, on the other hand, exists in a user's
novelty-seeking intent, which is manifested as a static and intrinsic user
preference for seeking novelty along with a dynamic session-based propensity.
To this end, we propose a novel hierarchical reinforcement learning-based
method to model the hierarchical user novelty-seeking intent, and to adapt the
recommendation policy accordingly based on the extracted user novelty-seeking
propensity. We further incorporate diversity and novelty-related measurement in
the reward function of the hierarchical RL (HRL) agent to encourage user
exploration \cite{chen2021values}. We demonstrate the benefits of explicitly
modeling hierarchical user novelty-seeking intent in recommendations through
extensive experiments on simulated and real-world datasets. In particular, we
demonstrate that the effectiveness of our proposed hierarchical RL-based method
lies in its ability to capture such hierarchically-structured intent. As a
result, the proposed HRL model achieves superior performance on several public
datasets, compared with state-of-art baselines
Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations
Existing aspect extraction methods mostly rely on explicit or ground truth
aspect information, or using data mining or machine learning approaches to
extract aspects from implicit user feedback such as user reviews. It however
remains under-explored how the extracted aspects can help generate more
meaningful recommendations to the users. Meanwhile, existing research on
aspect-based recommendations often relies on separate aspect extraction models
or assumes the aspects are given, without accounting for the fact the optimal
set of aspects could be dependent on the recommendation task at hand.
In this work, we propose to combine aspect extraction together with
aspect-based recommendations in an end-to-end manner, achieving the two goals
together in a single framework. For the aspect extraction component, we
leverage the recent advances in large language models and design a new prompt
learning mechanism to generate aspects for the end recommendation task. For the
aspect-based recommendation component, the extracted aspects are concatenated
with the usual user and item features used by the recommendation model. The
recommendation task mediates the learning of the user embeddings and item
embeddings, which are used as soft prompts to generate aspects. Therefore, the
extracted aspects are personalized and contextualized by the recommendation
task. We showcase the effectiveness of our proposed method through extensive
experiments on three industrial datasets, where our proposed framework
significantly outperforms state-of-the-art baselines in both the personalized
aspect extraction and aspect-based recommendation tasks. In particular, we
demonstrate that it is necessary and beneficial to combine the learning of
aspect extraction and aspect-based recommendation together. We also conduct
extensive ablation studies to understand the contribution of each design
component in our framework
Elastic multi-resolution model-serving to compute inferences
Machine-learning models are consuming an increasing fraction of the world\u27s computing resources. The cost of computing inferences with some machine-learning models is extremely high. Provisioning computing resources for peak performance, e.g., high availability and quality of service, entails the creation of headroom for traffic spikes (increases in demand) and preparing for the possibility of outages (decreases in capacity). Executing computer applications that utilize machine-learning models, also known as machine-learned models, can require significant capital and operational expenses.
This disclosure describes techniques to optimize use of computing resources for a machine-learning model. Multi-resolution models and/or models with recurrence are utilized. These models can compute inferences to varying degrees of quality (resolution). The multi-resolution models are served in an elastic manner such that a model of a resolution that fits both the available computing resources and is utilized to compute inferences
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