activity
20182021
most citedInteraction Embeddings for Prediction and Explanation in Knowledge Graphs

150 citations · 192 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SI202121 cited

Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks

Bibek Paudel, Abraham Bernstein

Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users. This results in recommendations that are hig…

cs.CY2019

Cross-Cutting Political Awareness through Diverse News Recommendations

Bibek Paudel, Abraham Bernstein

The suggestions generated by most existing recommender systems are known to suffer from a lack of diversity, and other issues like popularity bias. As a result, they have been obse…

cs.AI201921 cited

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

Wen Zhang, Bibek Paudel, Liang Wang +5

Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings c…

cs.AI2019150 cited

Interaction Embeddings for Prediction and Explanation in Knowledge Graphs

Wen Zhang, Bibek Paudel, Wei Zhang +2

Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions --- bi-di…

cs.IR2018

Loss Aversion in Recommender Systems: Utilizing Negative User Preference to Improve Recommendation Quality

Bibek Paudel, Sandro Luck, Abraham Bernstein

Negative user preference is an important context that is not sufficiently utilized by many existing recommender systems. This context is especially useful in scenarios where the co…