  An automated contextual suggestion algorithm is likely to recommend
contextually appropriate and personalized 'points-of-interest' (POIs) to a
user, if it can extract information from the user's preference history
(exploitation) and effectively blend it with the user's current contextual
information (exploration) to predict a POI's 'appropriateness' in the current
context. To balance this trade-off between exploitation and exploration, we
propose an unsupervised, generic framework involving a factored relevance model
(FRLM), constituting two distinct components, one pertaining to historical
contexts, and the other corresponding to the current context. We further
generalize the proposed FRLM by incorporating the semantic relationships
between terms in POI descriptors using kernel density estimation (KDE) on
embedded word vectors. Additionally, we show that trip-qualifiers, (e.g.
'trip-type', 'accompanied-by') are potentially useful information sources that
could be used to improve the recommendation effectiveness. Using such
information is not straight forward since users' texts/reviews of visited POIs
typically do not explicitly contain such annotations. We undertake a weakly
supervised approach to predict the associations between the review-texts in a
user profile and the likely trip contexts. Our experiments, conducted on the
TREC contextual suggestion 2016 dataset, demonstrate that factorization,
KDE-based generalizations, and trip-qualifier enriched contexts of the
relevance model improve POI recommendation.
