Method for facilitating and analyzing social interactions and context for targeted recommendations in a network of a telecom service provider

1. A method for providing dynamically and autonomously generated targeted recommendations about a social network facilitated event to a user in a social network and in a network compatible with said social network, wherein said social network is accessible from a mobile device in a mobile network, said method comprising providing recommendations about said event to said user based on a plurality of factors, said plurality of factors comprising of at least one among:

a relationship of said user with a social cluster;

a relationship of said event with an event cluster;

a relationship of said social cluster with said event cluster; and

ratings provided by a plurality of users for a plurality of events in said event cluster, wherein said user may be included in said plurality of users,

wherein said social cluster is created by performing steps comprising of:

analyzing feedback and rated events, wherein said rated events are similar to said event, to build a list of users with similar interests in a dynamic manner;

grouping dynamically said list of users in a dynamic manner into said social cluster; and

determining a final user rating for said rated events dependent on said social cluster to provide recommendations,

wherein said social cluster is modified by performing steps comprising of:

analyzing call detail records of said user, a list of contacts of said user, a profile of said user, a location of said user, said feedback and said rated events, wherein said rated events are similar to said event; and

re-grouping said social cluster to provide feedback and ratings for said rated events,

wherein said ratings modify an event cluster, and

wherein analyzing said call detail records comprises:

assigning weights to entries present in said call detail records; and

ranking said entries based on said weights, wherein said weights are assigned based on similarity of users between said user and said entries.

2. The method of claim 1 , wherein said mobile device uses Short Messaging Service (SMS) for accessing said social network, and wherein a copy of a SMS sent by a user is stored.

3. The method of claim 1 , wherein said event cluster is created by performing steps comprising of:

fetching event attributes of said event;

analyzing said event attributes and rating depending on a social cluster to build a list of events with similar attributes, wherein said event attributes is at least one of keywords, user profile, and location; and

dynamically grouping said list of events into said event clusters.

4. The method of claim 3 , wherein said event attributes are fetched dynamically from an event database, wherein said event attributes are entered by at least one of commercial and non-commercial enterprise, and stored in said event database.

5. The method of claim 3 , wherein said event attributes comprise of at least one of keywords, suitable user profiles, and location wherein said keywords is used to query for recommendations.

6. The method of claim 1 , wherein analyzing rated events further comprises of:

fetching users who have rated events, wherein said rated events are of interest to said user; and

quantifying interest of said users on said social network.

7. The method of claim 1 , wherein analyzing list of contacts of said user further comprises of:

checking contacts of said user for contacts who have similar interests as said user; and

quantifying influence of contacts with similar interests on said user.

8. The method of claim 1 , wherein providing recommendations to a user further comprises at least one of:

said user providing feedback about said event; and

said user providing ratings for said event.

9. The method of claim 8 , wherein said feedback and said ratings are further used to modify said event clusters in relation to said event.

10. The method of claim 9 , wherein modifying said event clusters comprises of:

dynamically modifying weights assigned to said event; and

dynamically modifying relationship between said event clusters and said social clusters.

11. A system for providing dynamically and autonomously generated targeted recommendations about a social network facilitated event to a user in a social network and in a network compatible with said social network accessed on at least one hardware device comprising a plurality of modules and a plurality of databases, wherein said social network is accessible from a mobile device in a mobile network, and wherein said system comprises a recommender module running on said at least one hardware device for providing recommendations about said event to said user based on a plurality of factors, said plurality of factors comprising of at least one of:

a relationship of said user with a social cluster;

a relationship of said event with an event cluster;

a relationship of said social cluster with said event cluster; and

ratings provided by a plurality of users for a plurality of events in said event cluster,

wherein said user may be included in said plurality of users;

wherein said at least one hardware device is adapted for creating said social cluster by performing steps comprising of:

analyzing feedback and rated events, wherein said rated events are similar to said event, to build a list of users with similar interests in a dynamic manner;

grouping dynamically said list of users in a dynamic manner into said social cluster; and

determining a final user rating for said rated events dependent on said social cluster to provide recommendations,

wherein said at least one hardware device is adapted for modifying said social cluster by performing steps comprising of:

analyzing call detail records of said user, a list of contacts of said user, a profile of said user, a location of said user, said feedback and said rated events, wherein said rated events are similar to said event;

re-grouping said social cluster to provide feedback and ratings for said rated events; and

determining a final user rating for said rated events dependent on said social cluster to provide recommendations,

wherein said at least one hardware device is adapted for analyzing call detail records by performing steps comprising of:

assigning weights to entries present in said call detail records; and

ranking said entries based on said weights, wherein said weights are assigned based on similarity of users between said user and said entries.

12. The system of claim 11 , wherein said mobile device is adapted for using Short Messaging Service (SMS) for accessing said social network, and wherein a copy of a SMS sent by a user is stored.

13. The system of claim 11 , wherein said at least one hardware device is adapted for creating said event cluster by performing steps comprising of:

fetching event attributes of said event;

analyzing said event attributes to build a list of events and rating said events depending on a social cluster to build a list of events with similar attributes, wherein said attributes is at least one of keywords, user profile, and location; and

dynamically grouping said list of events into said event clusters.

14. The system of claim 13 , wherein said at least one hardware device is adapted for fetching dynamically said event attributes from an event database, wherein said event attributes are entered by at least one of commercial and non-commercial enterprise.

15. The system of claim 11 , wherein said at least one hardware device is adapted for analyzing events by performing steps comprising of:

sorting events by rating at least one of timestamps, and alphabets;

fetching users who have rated events, wherein said events are of interest to said user; and

quantifying interest of said users on said social network.

16. The system of claim 11 , wherein said at least one hardware device is adapted for analyzing list of contacts of said user by performing steps comprising of:

checking contacts of said user for contacts who have similar interests as said user; and

quantifying influence of contacts with similar interests on said user.

17. The system of claim 11 , wherein said at least one hardware device is adapted for modifying said event clusters based on feedback and ratings provided by said user.