Abstract: In some aspects, the techniques described herein relate to a method including: receiving, by a collaboration service, location data of a user, wherein the location data includes a timestamp; verifying, by the collaboration service and based on a digital itinerary associated with the user, a location of the user; processing data from a data profile associated with the user as input data to a machine learning model; receiving, by the collaboration service and as output of the machine learning model, a plurality of predicted travel objective classifications; determining, by the collaboration service, a plurality of travel objectives, wherein each of the plurality of travel objectives is associated with one of the plurality of predicted travel objective classifications; determining, by the collaboration service, a travel objective within a predefined proximity of the location of the user; and displaying the travel objective to the user via a planning interface.

Abstract: In some aspects, the techniques described herein relate to a method including: receiving, by a collaboration service, location data of a user, wherein the location data includes a timestamp; verifying, by the collaboration service and based on a digital itinerary associated with the user, a location of the user; processing data from a data profile associated with the user as input data to a machine learning model; receiving, by the collaboration service and as output of the machine learning model, a plurality of predicted travel objective classifications; determining, by the collaboration service, a plurality of travel objectives, wherein each of the plurality of travel objectives is associated with one of the plurality of predicted travel objective classifications; determining, by the collaboration service, a travel objective within a predefined proximity of the location of the user; and displaying the travel objective to the user via a planning interface.

Abstract: In some aspects, the techniques described herein relate to a method including: providing one or more curation content machine learning models, wherein the one or more curation content machine learning models are configured to classify a textual review into a plurality of classifications, and wherein the plurality of classifications have been assigned respective relative weights; receiving, at a collaboration service, a user review of a travel objective from a user of the collaboration service; providing the review as an input feature dataset to the one or more curation content machine learning models; receiving, as output of the one or more curation content machine learning models, a plurality of classifications of the user review; and aggregating, by the collaboration service, a relative value score based on the assigned relative weight of the plurality of classifications of the user review.

Abstract: In some aspects, the techniques described herein relate to a method including: providing, in a collaboration space, a point conversion interface; passing, via the point conversion interface, user credentials to a third-party point issuer; receiving, via the point conversion interface and from the third-party point issuer, a number of points, wherein the number of points is associated with the user credentials; multiplying the number of points by a conversion factor, wherein the multiplying produces a number of unified points; crediting a points pool associated with a collaboration space, with the number of unified points; and receiving, at the collaboration space and from a user of the collaboration space, an instruction to apply the number of unified points to a travel objective, wherein the travel objective is included on a digital itinerary provided by the collaboration space.

Abstract: In some aspects, the techniques described herein relate to a method including: providing, in a collaboration space, a point conversion interface; passing, via the point conversion interface, user credentials to a third-party point issuer; receiving, via the point conversion interface and from the third-party point issuer, a number of points, wherein the number of points is associated with the user credentials; multiplying the number of points by a conversion factor, wherein the multiplying produces a number of unified points; crediting a points pool associated with a collaboration space, with the number of unified points; and receiving, at the collaboration space and from a user of the collaboration space, an instruction to apply the number of unified points to a travel objective, wherein the travel objective is included on a digital itinerary provided by the collaboration space.

Abstract: In some aspects, the techniques described herein relate to a method including: providing, in a collaboration space, a point conversion interface; passing, via the point conversion interface, user credentials to a third-party point issuer; receiving, via the point conversion interface and from the third-party point issuer, a number of points, wherein the number of points is associated with the user credentials; multiplying the number of points by a conversion factor, wherein the multiplying produces a number of unified points; crediting a points pool associated with a collaboration space, with the number of unified points; and receiving, at the collaboration space and from a user of the collaboration space, an instruction to apply the number of unified points to a travel objective, wherein the travel objective is included on a digital itinerary provided by the collaboration space.

Abstract: In some aspects, the techniques described herein relate to a method including: providing one or more curation content machine learning models, wherein the one or more curation content machine learning models are configured to classify a textual review into a plurality of classifications, and wherein the plurality of classifications have been assigned respective relative weights; receiving, at a collaboration service, a user review of a travel objective from a user of the collaboration service; providing the review as an input feature dataset to the one or more curation content machine learning models; receiving, as output of the one or more curation content machine learning models, a plurality of classifications of the user review; and aggregating, by the collaboration service, a relative value score based on the assigned relative weight of the plurality of classifications of the user review.

Abstract: In some aspects, the techniques described herein relate to a method including: interrelating, in a database, respective data profiles for each of a plurality of users of a collaboration platform; processing data from each of the respective data profiles as input data to a machine learning model; receiving, as output of the machine learning model, a plurality of predicted travel objective classifications; displaying, via a planning interface of a collaboration space of the collaboration platform, a plurality of travel objectives, wherein each of the plurality of travel objectives is associated with one of the plurality of predicted travel objective classifications; receiving, from a first user of the plurality of users, input with respect to a first travel objective of the plurality of travel objectives; and adding the first travel objective to a digital itinerary maintained by the collaboration space.

Abstract: In some aspects, the techniques described herein relate to a method including: receiving, by a collaboration service, location data of a user, wherein the location data includes a timestamp; verifying, by the collaboration service and based on a digital itinerary associated with the user, a location of the user; processing data from a data profile associated with the user as input data to a machine learning model; receiving, by the collaboration service and as output of the machine learning model, a plurality of predicted travel objective classifications; determining, by the collaboration service, a plurality of travel objectives, wherein each of the plurality of travel objectives is associated with one of the plurality of predicted travel objective classifications; determining, by the collaboration service, a travel objective within a predefined proximity of the location of the user; and displaying the travel objective to the user via a planning interface.

Abstract: In some aspects, the techniques described herein relate to a method including: providing, in a collaboration space, a point conversion interface; passing, via the point conversion interface, user credentials to a third-party point issuer; receiving, via the point conversion interface and from the third-party point issuer, a number of points, wherein the number of points is associated with the user credentials; multiplying the number of points by a conversion factor, wherein the multiplying produces a number of unified points; crediting a points pool associated with a collaboration space, with the number of unified points; and receiving, at the collaboration space and from a user of the collaboration space, an instruction to apply the number of unified points to a travel objective, wherein the travel objective is included on a digital itinerary provided by the collaboration space.