Search Results for author:
Found 5 papers, 0 papers with code
Detect Professional Malicious User with Metric Learning in Recommender Systems
2) the PMU detection model should take both ratings and reviews into consideration, which makes PMU detection a multi-modal problem.
A Unified Collaborative Representation Learning for Neural-Network based Recommender Systems
On the other hand, ME models directly employ inner products as a default loss function metric that cannot project users and items into a proper latent space, which is a methodological disadvantage.
Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems
Classical accuracy-oriented Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predictable recommendations, making them boring and unsatisfied.
The resonances $X(4140)$, $X(4160)$, and $P_{cs}(4459)$ in the decay of $Λ_b\to J/ψΛφ$
We study the decay of $\Lambda_b\to J/\psi\Lambda\phi$ by taking into account the intermediate resonances $X(4140)$, $X(4160)$, and $P_{cs}(4459)$.
High Energy Physics - Phenomenology
Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing
Since the sensed data from different cells (sub-areas) of the target sensing area will probably lead to diverse levels of inference data quality, cell selection (i. e., choose which cells of the target area to collect sensed data from participants) is a critical issue that will impact the total amount of data that requires to be collected (i. e., data collection costs) for ensuring a certain level of quality.
