  The revolution of World Wide Web (WWW) and smart-phone technologies have been
the key-factor behind remarkable success of social networks. With the ease of
availability of check-in data, the location-based social networks (LBSN) (e.g.,
Facebook1, etc.) have been heavily explored in the past decade for
Point-of-Interest (POI) recommendation. Though many POI recommenders have been
defined, most of them have focused on recommending a single location or an
arbitrary list that is not contextually coherent. It has been cumbersome to
rely on such systems when one needs a contextually coherent list of locations,
that can be used for various day-to-day activities, for e.g., itinerary
planning. This paper proposes a model termed as CAPS (Context-Aware
Personalized POI Sequence Recommender System) that generates contextually
coherent POI sequences relevant to user preferences. To the best of our
knowledge, CAPS is the first attempt to formulate the contextual POI sequence
modeling by extending Recurrent Neural Network (RNN) and its variants. CAPS
extends RNN by incorporating multiple contexts to the hidden layer and by
incorporating global context (sequence features) to the hidden layers and the
output layer. It extends the variants of RNN (e.g., Long-short term memory
(LSTM)) by incorporating multiple contexts and global features in the gate
update relations. The major contributions of this paper are: (i) it models the
contextual POI sequence problem by incorporating personalized user preferences
through multiple constraints (e.g., categorical, social, temporal, etc.), (ii)
it extends RNN to incorporate the contexts of individual item and that of the
whole sequence. It also extends the gated functionality of variants of RNN to
incorporate the multiple contexts, and (iii) it evaluates the proposed models
against two real-world data sets.
