Preference engine for generating predictions on entertainment products of services

• US 20050197961A1
• Filed: 03/08/2005
• Published: 09/08/2005
• Est. Priority Date: 03/08/2004
• Status: Abandoned Application

• Current Assignee: Fluent Mobile Incorporated
• Original Assignee: Fluent Mobile Incorporated
• Inventors: Miller, Gregory P., Miller, Michael R.
• Application Number:
  US11/073,727
• Publication Number:
  US 20050197961A1
• Time in Patent Office: Days
• US Class Current: 705/52
• CPC Class Codes: G06F 16/435 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/634 Query by example, e.g. query by humming
• CPC Class Codes: G06F 16/635 Filtering based on additional data, e.g. user or group profiles

Abstract

A preference predicting method compares a subject user'"'"'s play list with a plurality of other user'"'"'s play lists and generates suggested new entertainment product or service selections to the subject user. In an embodiment of the method, the user'"'"'s play list is compared to stored play lists to identify, on a selection by selection basis, how many selection titles from the user are found on each of the stored play lists. This comparison step generates a peer comparison group of the stored play lists having at least a selected number (e.g., fifty) selection title matches. The peer comparison group entries having a selected number of the user'"'"'s play list selection titles are identified as liking the same selections and each identified play list is searched to identify a selection title not included in the user'"'"'s play list, thereby generating a predicted selection title for the subject user.

Claims

1. A predictive method for a subject user or consumer to identify new entertainment products or services from a plurality of possible choices, comprising the method steps of:

(a) assembling a control list having a selected plurality of entries identifying representative entertainment products or services for said subject user;

(b) identifying a peer comparison group for said subject user;

(c) comparing said subject user'"'"'s control list of representative entertainment products or services with a selected number of control lists for a selected population of other user'"'"'s to generate a peer comparison group having a selected number of matching control list entries;

(d) identifying non-similar entries from said peer comparison group'"'"'s control lists;

(e) sorting said non-similar entries from said peer comparison group'"'"'s control lists to generate a list of entries corresponding to best predictions for new entertainment products or services not yet identified with said subject user;

(f) selecting at least one entry from said list of entries corresponding to best predictions for new entertainment products or services not yet identified with said subject user; and

(g) reporting said at least one entry to said subject user.

2. The predictive method of claim 1, wherein step (b), identifying a peer comparison group for said subject user, comprises:

(a1) selecting statistically relevant number of users for selection as peers for said subject user from a system population comprising a plurality of user control lists;

(a2) identifying a selected number of song matches between said subject user and the rest of the users in said system population to select a statistically relevant peer comparison group of users who like a selected minimum number of the same entries; and

(a3) selecting users for the peer comparison group having at least said minimum number of the same entries.

3. The predictive method of claim 1, wherein step (d), identifying non-similar entries from said peer comparison group'"'"'s control lists, further comprises:

(d1) identifying entries in the Peer Group lists that are not in the user'"'"'s control list of entries to generate a peer non-similar list for each list in the comparison group; and

(d2) identifying the most often occurring or popular entries among the peers'"'"' non-similar lists.

4. The predictive method of claim 1, wherein the method is executed in a web-based transaction session and step (g), reporting said at least one entry corresponding to a best prediction to said subject user, comprises:

providing session results as recommended entertainment products or services to the subject user.