Systems and methods to generate record recommendations for users of collaboration environment

1 . A system configured to generate record recommendations for users of a collaboration environment, the system comprising:

non-transitory electronic storage storing environment state information maintaining a collaboration environment, the environment state information including work unit records, the work unit records describing units of work managed, created, and/or assigned within the collaboration environment; and

one or more physical processors configured by machine-readable instructions to:

manage the environment state information to facilitate interaction by users with the collaboration environment through instances of a graphical user interface;

establish one or more network connections with client computing platforms associated with the users;

effectuate communication of user interface information to the client computing platforms over the one or more network connections to cause the client computing platforms to present the instances of the graphical user interface through which the users interact with the work unit records;

generate interaction information for the users based on monitoring change in the environment state information over time derived from interactions by the users with the collaboration environment via the instances of the graphical user interface, the interaction information including interaction values for an interaction attribute that characterizes the interactions of individual ones of the users with individual ones of the work unit records in individual ones of the instances of the graphical user interface, an individual interaction value corresponding to an individual one of the work unit records and an individual one of the users, the interaction information including a first interaction value for a first user corresponding to a first work unit record, a second interaction value for a second user corresponding to the first work unit record, and a third interaction value for the second user corresponding to a second work unit record;

for individual users:

rank the work unit records that the individual users have yet to interact with, but similar users have, based on the interaction information for the similar users; and

select one or more relatively highest ranked work unit records to be included in record recommendations for the individual users;

generate, for the individual users, the record recommendations based on the one or more relatively highest ranked work unit records, an individual record recommendation including a recommendation for an individual user to interact with an individual relatively highest ranked work unit record that the individual user has yet to interact with but a similar user has, such that for the first user, generate a first record recommendation to interact with the second work unit record based on the first user being similar to the second user and the first user having yet to interact with the second work unit record and the second work unit record being relatively highest ranked for the first user; and

update the user interface information to cause the instances of the graphical user interface to present the record recommendations, and wherein selection of individual ones of the record recommendations causes the instances of the graphical user interface to navigate to individual work unit pages corresponding to the individual ones of the record recommendations.

2 . The system of claim 1 , wherein the one or more physical processors are further configured by the machine-readable instructions to:

determine, for the individual users, predicted interaction values for the work unit records the individual users have yet to interact with based on the interaction information for the similar users, including determine a first predicted interaction value for the first user with respect to the second work unit record based on the third interaction value for the second user with respect to the second work unit record.

3 . The system of claim 2 , wherein the one or more physical processors are further configured by the machine-readable instructions to:

generate, for the individual users, the record recommendations further based on the predicted interaction values for the work unit records the individual users have yet to interact with.

4 . The system of claim 3 , wherein the one or more physical processors are further configured by the machine-readable instructions to:

for the individual users:

rank the work unit records that the individual users have yet to interact with further based on the predicted interaction values.

5 . The system of claim 1 , wherein the interactions are classified by interaction types, the interaction types include visitation type, sharing type, collaboration type, and multi-homing type.

6 . The system of claim 5 , wherein:

the interaction values of the visitation type convey quantity of visitations to work unit pages of the work unit records by the users;

the interaction values of the sharing type convey quantity of sharing instances of the work unit pages of the work unit records by the users;

the interaction values of the collaboration type convey quantity of comments within the work unit pages of the work unit records by the users; and

the interaction values of the multi-homing type convey quantity of comments within the work unit pages of the work unit records by the users.

7 . The system of claim 5 , wherein individual interaction values are generated by aggregating sets of interaction values determined per work unit record per interaction type.

8 . The system of claim 7 , wherein aggregating includes weighting the individual interaction values in the sets of interaction values determined per work unit record based on the interaction types of the interactions with the work unit records.

9 . The system of claim 1 , wherein the one or more physical processors are further configured by the machine-readable instructions to:

determine, for the individual users, recommendation context conveying user states with respect to interaction with the collaboration environment; and

determine user similarity based on the recommendation context of the individual users.

10 . A method to generate record recommendations for users of a collaboration environment, the method being implemented in a computer system comprising non-transitory electronic storage and one or more physical processors configured by machine-readable instructions, the method comprising:

managing, by the one or more physical processors, environment state information stored in the non-transitory electronic storage, the environment state information maintaining a collaboration environment, the collaboration environment being configured to facilitate interaction by users with the collaboration environment through instances of a graphical user interface, the environment state information including work unit records, the work unit records describing units of work managed, created, and/or assigned within the collaboration environment;

establishing one or more network connections with client computing platforms associated with the users;

effectuating communication of user interface information from the one or more physical processors to the client computing platforms over the one or more network connections to cause the client computing platforms to present the instances of the graphical user interface through which the users interact with the work unit records;

generating interaction information for the users based on monitoring change in the environment state information over time derived from interactions by the users with the collaboration environment via the instances of the graphical user interface, the interaction information including interaction values for an interaction attribute that characterizes the interactions of individual ones of the users with individual ones of the work unit records in individual ones of the instances of the graphical user interface, an individual interaction value corresponding to an individual one of the work unit records and an individual one of the users, the interaction information including a first interaction value for a first user corresponding to a first work unit record, a second interaction value for a second user corresponding to the first work unit record, and a third interaction value for the second user corresponding to a second work unit record;

for individual users:

ranking the work unit records that the individual users have yet to interact with, but similar users have, based on the interaction information for the similar users; and

selecting one or more relatively highest ranked work unit records to be included in record recommendations for the individual users;

generating, for the individual users, the record recommendations based on the one or more relatively highest ranked work unit records, an individual record recommendation including a recommendation for an individual user to interact with an individual relatively highest ranked work unit record that the individual user has yet to interact with but a similar user has, including generating, for the first user, a first record recommendation to interact with the second work unit record based on the first user being similar to the second user and the first user having yet to interact with the second work unit record and the second work unit record being relatively highest ranked for the first user; and

updating the user interface information to cause the instances of the graphical user interface to present the record recommendations, and wherein selection of individual ones of the record recommendations causes the instances of the graphical user interface to navigate to individual work unit pages corresponding to the individual ones of the record recommendations.

11 . The method of claim 10 , further comprising:

determining, for the individual users, predicted interaction values for the work unit records the individual users have yet to interact with based on the interaction information for the similar users, including determining a first predicted interaction value for the first user with respect to the second work unit record based on the third interaction value for the second user with respect to the second work unit record.

12 . The method of claim 11 , further comprising:

generating, for the individual users, the record recommendations further based on the predicted interaction values for the work unit records the individual users have yet to interact with.

13 . The method of claim 12 , further comprising:

for the individual users:

performing the ranking of the work unit records that the individual users have yet to interact with further based on the predicted interaction values.

14 . The method of claim 10 , wherein the interactions are classified by interaction types, the interaction types include visitation type, sharing type, collaboration type, and multi-homing type.

15 . The method of claim 14 , wherein:

the interaction values of the visitation type convey quantity of visitations to work unit pages of the work unit records by the users;

the interaction values of the sharing type convey quantity of sharing instances of the work unit pages of the work unit records by the users;

the interaction values of the collaboration type convey quantity of comments within the work unit pages of the work unit records by the users; and

the interaction values of the multi-homing type convey quantity of comments within the work unit pages of the work unit records by the users.

16 . The method of claim 14 , wherein individual interaction values are generated by aggregating sets of interaction values determined per work unit record per interaction type.

17 . The method of claim 16 , wherein the aggregating includes weighting the individual interaction values in the sets of interaction values determined per work unit record based on the interaction types of the interactions with the work unit records.

18 . The method of claim 10 , further comprising:

determining, for the individual users, recommendation context conveying user states with respect to interaction with the collaboration environment; and

determining user similarity based on the recommendation context of the individual users.