Abstract: In accordance with disclosed embodiments, there are provided methods, systems, and apparatuses for performing time-partitioned collaborative filtering in an on-demand service environment including, for example, receiving as input, a plurality of access requests for data stored within the host organization and a corresponding plurality of actions for the data to which access is requested; accessing an input table having a time field, action field, item field, and agent field therein; recording time data and agent data for each of the received plurality of access requests and the corresponding plurality of actions; recording an item within the item field and an action within the action field for each of the received plurality of access requests and the corresponding plurality of actions based on the action performed on an item of the data to which access is requested; and analyzing the input table to generate one or more pairs of first actions and items to second actions and items and a time based score for eac

Abstract: Disclosed are methods, apparatus, systems, and computer-readable storage media for recommending an event to a user. In some implementations, one or more servers receive information identifying a plurality of events. The one or more servers store data of the plurality of events in a first one or more data tables having an action field, an intent field, and a user field, and analyze the data of the first one or more data tables to generate one or more pairs, each pair including information identifying a set of events and a target event. The one or more servers may calculate a similarity score for each of the one or more pairs and store the respective similarity score in a second one or more data tables having a set field, a target event field, and a similarity score field.

Abstract: Disclosed are methods, apparatus, systems, and computer-readable storage media for generating or updating sets of events. In some implementations, a database storing data records representing events can be maintained. A selection of a first event can be processed. A first timestamp associated with the first event can be identified. A first set of events comprising the first event a first portion of other events can be generated or updated. A second set of events comprising the first event a second portion of other events can be generated or updated. A first frequency of matched events can be determined for the first set of events. A second frequency of matched events can be determined for the second set of events. It can be determined that the first frequency is greater than the second frequency. A similarity associated with the first event can be generated or updated. The first set of events can be provided to a client device.

Abstract: Disclosed are methods, apparatus, systems, and computer-readable storage media for recommending an event to a user. In some implementations, one or more servers receive information identifying a plurality of events. The one or more servers store data of the plurality of events in a first one or more data tables having an action field, an intent field, and a user field, and analyze the data of the first one or more data tables to generate one or more pairs, each pair including information identifying a set of events and a target event. The one or more servers may calculate a similarity score for each of the one or more pairs and store the respective similarity score in a second one or more data tables having a set field, a target event field, and a similarity score field.

Abstract: Disclosed are methods, apparatus, systems, and computer-readable storage media for recommending an event to a user. In some implementations, one or more servers receive information identifying a plurality of events. The one or more servers store data of the plurality of events in a first one or more data tables having an action field, an item field, and a user field, and analyze the data of the first one or more data tables to generate one or more pairs, each pair including information identifying a set of events and a target event. The one or more servers may calculate a similarity score for each of the one or more pairs and store the respective similarity score in a second one or more data table having a set field, a target event field, and a similarity score field.

Abstract: In accordance with disclosed embodiments, there are provided methods, systems, and apparatuses for performing time-partitioned collaborative filtering in an on-demand service environment including, for example, receiving as input, a plurality of access requests for data stored within the host organization and a corresponding plurality of actions for the data to which access is requested; accessing an input table having a time field, action field, item field, and agent field therein; recording time data and agent data for each of the received plurality of access requests and the corresponding plurality of actions; recording an item within the item field and an action within the action field for each of the received plurality of access requests and the corresponding plurality of actions based on the action performed on an item of the data to which access is requested; and analyzing the input table to generate one or more pairs of first actions and items to second actions and items and a time based score for eac

Abstract: Disclosed are methods, apparatus, systems, and computer-readable storage media for recommending an event to a user. In some implementations, one or more servers receive information identifying a plurality of events. The one or more servers store data of the plurality of events in a first one or more data tables having an action field, an item field, and a user field, and analyze the data of the first one or more data tables to generate one or more pairs, each pair including information identifying a set of events and a target event. The one or more servers may calculate a similarity score for each of the one or more pairs and store the respective similarity score in a second one or more data table having a set field, a target event field, and a similarity score field.

Abstract: In accordance with disclosed embodiments, there are provided methods, systems, and apparatuses for performing time-partitioned collaborative filtering in an on-demand service environment including, for example, receiving as input, a plurality of access requests for data stored within the host organization and a corresponding plurality of actions for the data to which access is requested; accessing an input table having a time field, action field, item field, and agent field therein; recording time data and agent data for each of the received plurality of access requests and the corresponding plurality of actions; recording an item within the item field and an action within the action field for each of the received plurality of access requests and the corresponding plurality of actions based on the action performed on an item of the data to which access is requested; and analyzing the input table to generate one or more pairs of first actions and items to second actions and items and a time based score for eac

Abstract: Disclosed are methods, apparatus, systems, and computer-readable storage media for recommending an event to a user. In some implementations, one or more servers receive information identifying a plurality of events. The one or more servers store data of the plurality of events in a first one or more data tables having an action field, an item field, and a user field, and analyze the data of the first one or more data tables to generate one or more pairs, each pair including information identifying a set of events and a target event. The one or more servers may calculate a similarity score for each of the one or more pairs and store the respective similarity score in a second one or more data tables having a set field, a target event field, and a similarity score field.

Abstract: Disclosed are methods, apparatus, systems, and computer-readable storage media for generating or updating sets of events. In some implementations, a database storing data records representing events can be maintained. A selection of a first event can be processed. A first timestamp associated with the first event can be identified. A first set of events comprising the first event a first portion of other events can be generated or updated. A second set of events comprising the first event a second portion of other events can be generated or updated. A first frequency of matched events can be determined for the first set of events. A second frequency of matched events can be determined for the second set of events. It can be determined that the first frequency is greater than the second frequency. A similarity associated with the first event can be generated or updated. The first set of events can be provided to a client device.

Abstract: Disclosed are systems, apparatus, methods and computer-readable storage media for combining topic suggestions from different topic sources to assign to textual data items. In some implementations, a first automated topic source provides one or more first topic suggestions to associate with a textual data item. Each first topic suggestion has a respective first characteristic, which can be adjusted. A second automated topic source provides one or more second topic suggestions to associate with the textual data item. Each second topic suggestion has a respective second characteristic, which can be adjusted. The first and second topic suggestions are combined to define a combined list. A reference value is determined or retrieved to impact ordering of the topic suggestions in the combined list.

Abstract: Disclosed are methods, apparatus, systems, and computer-readable storage media for identifying similar labels. In some implementations, one or more servers maintain a plurality of data entries in one or more database tables storing textual data, each data entry of a first portion of the data entries including: a text sequence, a label, and a text-to-label association score, and each data entry of a second portion of the data entries including: a first label, a second label, and a similarity score. The one or more servers analyze the data of the first portion of data entries to generate one or more pairs, each pair including information identifying a first label and a second label. The one or more servers calculate a similarity score for each of the one or more pairs and store the respective similarity scores in the second portion of the data entries.

Abstract: Disclosed are systems, apparatus, methods and computer-readable storage media for combining topic suggestions from different topic sources to assign to textual data items. In some implementations, a first automated topic source provides one or more first topic suggestions to associate with a textual data item. Each first topic suggestion has a respective first characteristic, which can be adjusted. A second automated topic source provides one or more second topic suggestions to associate with the textual data item. Each second topic suggestion has a respective second characteristic, which can be adjusted. The first and second topic suggestions are combined to define a combined list. A reference value is determined or retrieved to impact ordering of the topic suggestions in the combined list.

Abstract: Disclosed are some examples of systems, apparatus, methods and storage media for providing customized recommendations to users. Some implementations more particularly relate to a recommendation platform that enables authorized third parties to create, customize and add new recommendations that are then available to be served to target users or audiences of users. Some implementations further relate to a recommendation platform that enables authorized users to define audiences, scheduling settings, scheduling policies, and rules to customize or influence the provision of associated recommendations to the users. The recommendation platform includes a recommendation engine that serves the recommendations to users based on such defined audiences, scheduling settings, policies or other rules.

Abstract: Disclosed are methods, apparatus, systems, and computer-readable storage media for identifying a topic for a text. In some implementations, one or more servers maintain a plurality of data entries in one or more database tables storing text data, each data entry of a first portion of the data entries including: a text sequence, a topic, and a text-to-topic association score indicating a number of times that the text sequence appears in a processed text associated with the topic, each data entry of a second portion of the data entries including a total word score indicating a number of times that a respective text sequence appears in one or more processed texts. The one or more servers may receive an incoming text and identify a topic for the incoming text by processing the text sequences of the incoming text in relation to the data entries in the database tables.

Abstract: Disclosed are systems, apparatus, methods and computer-readable storage media for determining whether to continue relationships in an online social network. For example, a server can maintain data entries in one or more database tables. A given data entry can include: an identification of an entity, an identification of a user having a social networking relationship with the entity, and an affinity score indicating an amount of interaction by the user with the entity. For each entry, a ratio of the affinity score to a measure of an amount of content associated with the entity and published in a feed accessible by the user can be determined. Depending on the implementation, such ratios can be compared with a threshold and/or ranked to identify candidates for discontinuing social networking relationships.

Abstract: Methods and systems are provided for enhancing user engagement in a social network computing environment by using affinity indices to track the interaction among posts, topics, users, groups, and experts, and populating displays on user, group, and topic pages using the affinity indices.

Abstract: Disclosed are systems, apparatus, methods and computer-readable storage media for combining topic suggestions from different topic sources to assign to textual data items. In some implementations, a first automated topic source provides one or more first topic suggestions to associate with a textual data item. Each first topic suggestion has a respective first characteristic, which can be adjusted. A second automated topic source provides one or more second topic suggestions to associate with the textual data item. Each second topic suggestion has a respective second characteristic, which can be adjusted. The first and second topic suggestions are combined to define a combined list. A reference value is determined or retrieved to impact ordering of the topic suggestions in the combined list.

Abstract: Disclosed are methods, apparatus, systems, and computer-readable storage media for recommending a workflow to a user. In some implementations, one or more servers receive information identifying a plurality of events. The one or more servers store data of the plurality of events in a first one or more data tables having an action field, an item field, a user field, and a timestamp field, and analyze the data of the first one or more data tables to generate one or more pairs, each pair including information identifying an ordered set of events and a target event. The one or more servers calculate a similarity score for each of the one or more pairs and store the respective similarity scores in a second one or more data tables having a set field, a target event field, and a similarity score field.

Abstract: Disclosed are methods, apparatus, systems, and computer-readable storage media for recommending an event to a user. In some implementations, one or more servers receive information identifying a plurality of events. The one or more servers store data of the plurality of events in a first one or more data tables having an action field, an item field, and a user field, and analyze the data of the first one or more data tables to generate one or more pairs, each pair including information identifying a set of events and a target event. The one or more servers may calculate a similarity score for each of the one or more pairs and store the respective similarity score in a second one or more data tables having a set field, a target event field, and a similarity score field.