Vinayak Gupta

Vinayak Gupta, Srikanta Bedathur

Current approaches for points-of-interest (POI) recommendation learn the preferences of a user via the standard spatial features such as the POI coordinates, the social network, etc. These models ignore a crucial aspect of spatial mobility -- every user carries their smartphones wherever they go. In addition, with growing privacy concerns, users refrain from sharing their exact geographical coordinates and their social media activity. In this paper, we present REVAMP, a sequential POI recommendation approach that utilizes the user activity on smartphone applications (or apps) to identify their mobility preferences. This work aligns with the recent psychological studies of online urban users, which show that their spatial mobility behavior is largely influenced by the activity of their smartphone apps. In addition, our proposal of coarse-grained smartphone data refers to data logs collected in a privacy-conscious manner, i.e., consisting only of (a) category of the smartphone app and (b) category of check-in location. Thus, REVAMP is not privy to precise geo-coordinates, social networks, or the specific application being accessed. Buoyed by the efficacy of self-attention models, we learn the POI preferences of a user using two forms of positional encodings -- absolute and relative -- with each extracted from the inter-check-in dynamics in the check-in sequence of a user. Extensive experiments across two large-scale datasets from China show the predictive prowess of REVAMP and its ability to predict app- and POI categories.

Vinayak Gupta, Srikanta Bedathur, Abir De

Temporal sequences have become pervasive in various real-world applications. Consequently, the volume of data generated in the form of continuous time-event sequence(s) or CTES(s) has increased exponentially in the past few years. Thus, a significant fraction of the ongoing research on CTES datasets involves designing models to address downstream tasks such as next-event prediction, long-term forecasting, sequence classification etc. The recent developments in predictive modeling using marked temporal point processes (MTPP) have enabled an accurate characterization of several real-world applications involving the CTESs. However, due to the complex nature of these CTES datasets, the task of large-scale retrieval of temporal sequences has been overlooked by the past literature. In detail, by CTES retrieval we mean that for an input query sequence, a retrieval system must return a ranked list of relevant sequences from a large corpus. To tackle this, we propose NeuroSeqRet, a first-of-its-kind framework designed specifically for end-to-end CTES retrieval. Specifically, NeuroSeqRet introduces multiple enhancements over standard retrieval frameworks and first applies a trainable unwarping function on the query sequence which makes it comparable with corpus sequences, especially when a relevant query-corpus pair has individually different attributes. Next, it feeds the unwarped query sequence and the corpus sequence into MTPP-guided neural relevance models. We develop four variants of the relevance model for different kinds of applications based on the trade-off between accuracy and efficiency. We also propose an optimization framework to learn binary sequence embeddings from the relevance scores, suitable for the locality-sensitive hashing. Our experiments show the significant accuracy boost of NeuroSeqRet as well as the efficacy of our hashing mechanism.