====== Models ====== ``CAPRI`` contains various models for analysis of the data in Point-of-Interest (POI) datasets. All of the models are available in the `Models`_ directory of the ``CAPRI`` package. Please note that As the framework is still in development, we are working on adding more models. .. _Models: https://github.com/CapriRecSys/CAPRI/tree/main/Models Baselines --------- TBD Context-aware POI Recommendation -------------------------------- Context Aware Recommendation Systems incorporate a variety of contextual factors in order to accurately capture user preferences. GeoSoCa ~~~~~~~~~~~~~~~~ GoeSoCa is a novel POI recommendation method that uses geographical, social, and category correlations between users and POIs to make recommendations. These correlations can be learned from user check-in data on POIs in the past and used to predict a user's relevance score to an unvisited POI in order to offer suggestions to them. Read more at `GeoSoCa's paper `_ You can also check the content of `GeoSoCa`_ model in ``CAPRI`` package. .. _GeoSoCa: https://github.com/CapriRecSys/CAPRI/tree/main/Models/GeoSoCa LORE ~~~~~~~~~~~~~~~~ LORE is another model utilized in the context of context-aware POI recommendation systems. It is a popular and robust model for location recommendation focused on the impacts of geographical and social influence on users’ check-in behaviors. LORE incrementally mines sequential patterns from location sequences and represents the sequential patterns as a dynamic Location-Location Transition Graph (L2TG). It also predicts the probability of a user visiting a location by Additive Markov Chain (AMC) with L2TG. Finally, it fuses sequential influence with geographical influence and social influence into a unified recommendation framework. Read more at `LORE's paper `_ You can also check the content of `LORE`_ model in ``CAPRI`` package. .. _LORE: https://github.com/CapriRecSys/CAPRI/tree/main/Models/LORE USG ~~~~~~~~~~~~~~~~ USG is a well-known model in the POI recommedner community. Due to the spatial clustering phenomenon demonstrated in LBSN user check-in activities, USG places a specific emphasis on geographical impact in addition to deriving user preference based on researching social influence from peers. Accordingly, geographical influence among POIs has a significant impact on user check-in behaviors, which is modedl in USG using a power law distribution. This model creates a naïve Bayesian-based collaborative recommendation system based on geographical influence. Read more at `USG's paper `_ You can also check the content of `USG`_ model in ``CAPRI`` package. .. _USG: https://github.com/CapriRecSys/CAPRI/tree/main/Models/USG