17 citations · 34 across the 7 of their papers we have counts for
7 papers
Learning the Structure of Auto-Encoding Recommenders
Farhan Khawar, Leonard Kin Man Poon, Nevin Lianwen Zhang
Autoencoder recommenders have recently shown state-of-the-art performance in the recommendation task due to their ability to model non-linear item relationships effectively. Howeve…
Cleaned Similarity for Better Memory-Based Recommenders
Farhan Khawar, Nevin L. Zhang
Memory-based collaborative filtering methods like user or item k-nearest neighbors (kNN) are a simple yet effective solution to the recommendation problem. The backbone of these me…
Using Taste Groups for Collaborative Filtering
Farhan Khawar, Nevin L. Zhang
Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and domain independent. However, there is a lack of negative…
Matrix Factorization Equals Efficient Co-occurrence Representation
Farhan Khawar, Nevin L. Zhang
Matrix factorization is a simple and effective solution to the recommendation problem. It has been extensively employed in the industry and has attracted much attention from the ac…
Learning Hierarchical Item Categories from Implicit Feedback Data for Efficient Recommendations and Browsing
Farhan Khawar, Nevin L. Zhang
Searching, browsing, and recommendations are common ways in which the "choice overload" faced by users in the online marketplace can be mitigated. In this paper we propose the use…
Conformative Filtering for Implicit Feedback Data
Farhan Khawar, Nevin L. Zhang
Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and is domain independent. However, there is a lack of negat…
Latent Tree Models for Hierarchical Topic Detection
Peixian Chen, Nevin L. Zhang, Tengfei Liu +3
We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a c…