Author name cluster

Lile Li

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2020 Short Paper

Travel Time Prediction on Un-Monitored Roads: A Spatial Factorization Machine Based Approach (Student Abstract)

Lile Li
Wei Liu

Real-time trafﬁc monitoring is one of the most important factors for route planning and estimated time of arrival (ETA). Many major roads in large cities are installed with live trafﬁc monitoring systems, inferring the current trafﬁc congestion status and ETAs to other locations. However, there are also many other roads, especially small roads and paths, that are not monitored. Yet, live trafﬁc status on such un-monitored small roads can play a non-negligible role in personalized route planning and re-routing when road incident happens. How to estimate the trafﬁc status on such un-monitored roads is thus a valuable problem to be addressed. In this paper, we propose a model called Spatial Factorization Machines (SFM) to address this problem. A major advantage of the SFM model is that it incorporates physical distances and structures of road networks into the estimation of trafﬁc status on un-monitored roads. Our experiments on real world trafﬁc data demonstrate that the SFM model signiﬁcantly outperforms other existing models on ETA of un-monitored roads.

PDF Details

AAAI Conference 2019 Short Paper

Cross-Domain Recommendation via Coupled Factorization Machines

Lile Li
Quan Do
Wei Liu

Data across many business domains can be represented by two or more coupled data sets. Correlations among these coupled datasets have been studied in the literature for making more accurate cross-domain recommender systems. However, in existing methods, cross-domain recommendations mostly assume the coupled mode of data sets share identical latent factors, which limits the discovery of potentially useful domain-specific properties of the original data. In this paper, we proposed a novel cross-domain recommendation method called Coupled Factorization Machine (CoFM) that addresses this limitation. Compared to existing models, our research is the first model that uses factorization machines to capture both common characteristics of coupled domains while simultaneously preserving the differences among them. Our experiments with real-world datasets confirm the advantages of our method in making across-domain recommendations.

PDF Details