Systems and related methods of user-guided searching

A method, implemented by a computer system, relating to refining at least one document search by at least one user searcher during at least one individual search session, relating to finding documents relevant to the at least one search of the at least one user searcher during the at least one individual search session, comprising the following steps of:

• a) presenting to the at least one user searcher at least one large collection of documents comprising at least one set of document natural-language strings during the at least one individual search session, each document comprising at least one subset of the at least one set of document natural-language strings;
• b) wherein the at least one large collection of documents comprising at least one set of document natural-language strings during the at least one individual search session results from one search query of the at least one user searcher;
• c) gathering searcher overall evaluating relating to each single document of at least one sample subset of the at least one large collection of documents during the at least one individual search session;
• d) wherein the searcher overall evaluating comprises searcher-setting of at least one scalar relevancy-value or of at least one comparative relevancy-value for each document of the at least one sample subset during the at least one individual search session;
• e) using the gathered searcher overall evaluating relating to each single document of the at least one sample subset of the at least one large collection of documents, identifying at least one user searcher evaluation subset of relevancy-ordered document natural-language strings relating to the sampled documents;
• f) using the identified at least one user searcher evaluation subset of relevancy-ordered document natural-language strings relating to the sampled documents, identifying at least one relevancy order, approximating a preference function of the at least one user searcher, of the at least one large collection of documents resulting from the one search query, wherein the approximating the value function of the at least one user searcher comprises iterative application of the algorithm to maximizes ε subject to:
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• wherein
• i) i represents the valued object characteristic,
• ii) w represents the at least one overall relevancy weight value,
• iii) D represents an object described by an array of the valued object characteristics, and wherein D is at least one of additive form or multiplicative form, or any combination thereof,
• iv) ε represents the difference between the weighted values of all objects in the at least one large collection of objects,
• St represents the set of objects within a scalar category t,
• vi) T represents the total number of scalar categories,
• vii) M represents the total number of the valued object characteristics within the at least one large collection of objects,
• viii) PA and PB indicate a air of relatively compared objects with PA indicating the more preferred, and
• ix) R represents the total number of direct preference statements;
• and
• g) presenting, to the at least one user searcher during the at least one individual search session, at least one relevancy-ordered hierarchy of the at least one large collection of documents, resulting from the one search query, using the identified at least one relevancy order of the at least one large collection of objects.

A computer-implemented method comprising the following steps of:

• a) gathering preference feedback, relating to at least one object from at least one large collection of objects, from at least one user searcher;
• b) approximating a value function of the at least one user searcher based on the gathered preference feedback;
• and
• c) ordering the at least one large collection of objects based on the approximation of the value function of the at least one user searcher;
• d) wherein the step of approximating the value function of the at least one user searcher comprises iterative application of the algorithm to maximize ε subject to:
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• e) wherein
• i) i represents the valued object characteristic,
• ii) w represents the at least one overall relevancy weight value,
• iii) D represents an object described by an array of the valued object characteristics, and wherein D is at least one of additive form or multiplicative form, or any combination thereof,
• iv) ε represents the difference between the weighted values of all objects in the at least one large collection of objects,
• v) St represents the set of objects within a scalar category t,
• vi) T represents the total number of scalar categories,
• vii) M represents the total number of the valued object characteristics within the at least one large collection of objects,
• viii) PA and PB indicate a pair of relatively compared objects with PA indicating the more preferred, and
• ix) R represents the total number of direct preference statements;
• and
• f) wherein after the iterative application of the algorithm maximized ε, ordering the at least one large collection of objects based on the approximation of the value function of the at least one user searcher when occurs.

A computer system, relating to refining at least one search by at least one user searcher, relating to finding objects relevant to the at least one search of the at least one user searcher during at least one individual search session, comprising:

• a) at least one presenting computer interface adapted to present to the at least one user searcher at least one large collection of objects comprising at least one set of object characteristics during the at least one individual search session, each object comprising at least one subset of the at least one set of object characteristics;
• b) wherein the large collection of objects results from one search query of the at least one user searcher;
• c) at least one gathering computer interface adapted to gather searcher overall evaluating relating to each single object of at least one sample subset of the at least one large collection of objects;
• d) wherein the at least one gathering computer interface searcher overall evaluating comprises at least one gathering computer processor adapted to gather searcher-setting of at least one scalar relevancy-value or of at least one comparative relevancy-value relating to each object of the at least one sample subset during the at least one individual search session;
• e) at least one characteristic-evaluating computer processor, using the gathered searcher overall evaluating relating to each single object of the at least one sample subset of the at least one large collection of objects, adapted to identify at least one user searcher evaluation subset of relevancy-ordered characteristics relating to the sampled objects;
• f) at least one object-evaluating computer processor, using the identified at least one user searcher evaluation subset of relevancy-ordered characteristics relating to the sampled objects, adapted to identify at least one relevancy order, approximating a preference function of the at least one user searcher, of the at least one large collection of objects resulting from the one search query, wherein the approximating the value function of the at least one user searcher comprises iterative application of the algorithm to maximize ε subject to:
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• wherein
• i) i represents the valued object characteristic,
• ii) w represents the at least one overall relevancy weight value,
• iii) D represents an object described by an array of the valued object characteristics, and wherein D is at least one of additive form or multiplicative form, or any combination thereof,
• iv) ε represents the difference between the weighted values of all objects in the at least one large collection of objects,
• v) St represents the set of objects within a scalar category t,
• vi) T represents the total number of scalar categories,
• vii) M represents the total number of the valued object characteristics within the at least one large collection of objects,
• viii) PA and PB indicate a air of relatively compared with PA indicating the more preferred, and
• ix) R represents the total number of direct preference statements;
• and
• g) at least one re-presenting computer interface adapted to present, to the at least one user searcher during the at least one individual search session, at least one first relevancy-ordered hierarchy comprising the at least one large collection of objects resulting from the one search query, using the identified at least one relevancy order of the at least one large collection of objects.

A computer system, relating to refining at least one search by at least one user searcher during at least one individual search session, relating to finding objects relevant to the at least one search of the at least one user searcher, comprising:

• a) means for presenting to the at least one user searcher at least one large collection of objects comprising at least one set of object characteristics during the at least one individual search session, each object comprising at least one subset of the at least one set of object characteristics;
• b) wherein the large collection of objects results from one search query of the at least one user searcher;
• c) means for gathering searcher overall evaluating relating to each single object of at least one sample subset of the at least one large collection of objects;
• d) wherein the searcher overall evaluating comprises searcher-setting of at least one relevancy-value relating to each object of the at least one sample subset;
• e) means for using the gathered searcher overall evaluating relating to each single object of the at least one sample subset of the at least one large collection of objects, identifying at least one user searcher evaluation subset of relevancy-ordered characteristics relating to the sampled objects;
• f) means for, using the identified at least one user searcher evaluation subset of relevancy-ordered characteristics relating to the sampled objects, identifying at least one relevancy order, approximating a preference function of the at least one user searcher, of the at least one large collection of objects resulting from the one search query, wherein the approximating the value function of the at least one user searcher comprises iterative application of the algorithm to maximizes ε subject to:
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• wherein
• i) i represents the valued object characteristic,
• ii) w represents the at least one overall relevancy weight value,
• iii) D represents an object described by an array of the valued object characteristics, and wherein D is at least one of additive form or multiplicative form, or any combination thereof,
• iv) ε represents the difference between the weighted values of all objects in the at least one large collection of objects,
• v) St represents the set of objects within a scalar category t,
• vi) T represents the total number of scalar categories,
• vii) M represents the total number of the valued object characteristics within the at least one large collection of objects,
• viii) PA and PB indicate a pair of relatively compared objects with PA indicating the more preferred, and
• ix) R represents the total number of direct preference statements;
• and
• g) means for presenting, to the at least one user searcher during the at least one individual search session, at least one first relevancy-ordered hierarchy comprising the at least one large collection of objects resulting from the one search query, using the identified at least one relevancy order of the at least one large collection of objects.

A computer system, relating to refining at least one document search by at least one user searcher during at least one individual search session, relating to finding documents relevant to the at least one search of the at least one user searcher, comprising:

• a) means for presenting to the at least one user searcher during the at least one individual search session at least one large collection of documents comprising at least one set of document natural-language strings, each document comprising at least one subset of the at least one set of document natural-language strings;
• b) wherein the large collection of objects results from one search query of the at least one user searcher;
• c) means for gathering searcher overall evaluating relating to each single document of at least one sample subset of the at least one large collection of documents;
• d) wherein the searcher overall evaluating comprises searcher-setting of at least one scalar relevancy-value or of at least one comparative relevancy-value for each document of the at least one sample subset;
• e) means for, using the gathered searcher overall evaluating relating to each single document of the at least one sample subset of the at least one large collection of documents, identifying at least one user searcher evaluation subset of relevancy-ordered document natural-language strings relating to the sampled documents;
• f) means for, using the identified at least one user searcher evaluation subset of relevancy-ordered document natural-language strings relating to the sampled documents, identifying at least one relevancy order, approximating a preference function of the at least one user searcher, of the at least one large collection of documents resulting from the one search query, wherein the approximating the value function of the at least one user searcher comprises iterative application of the algorithm to maximize ε subject to:
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• wherein
• i) i represents the valued object characteristic,
• ii) w represents the at least one overall relevancy weight value,
• iii) D represents an object described by an array of the valued object characteristics, and wherein D is at least one of additive form or multiplicative form, or any combination thereof,
• iv) ε represents the difference between the weighted values of all objects in the at least one large collection of objects,
• v) St represents the set of objects within a scalar category t,
• vi) T represents the total number of scalar categories,
• vii) M represents the total number of the valued object characteristics within the at least one large collection of objects,
• viii) PA and PB indicate a air of relatively compared objects with PA indicating the more preferred, and
• ix) R represents the total number of direct preference statements;
• and
• g) means for presenting, to the at least one user searcher, at least one relevancy-ordered hierarchy of the at least one large collection of documents resulting from the one search query, using the identified at least one relevancy order of the at least one large collection of objects.

A method, implemented by a computer system, relating to refining at least one search by at least one user searcher during at least one individual search session, relating to finding objects relevant to the at least one search of the at least one user searcher, comprising the following steps of:

• a) presenting to the at least one user searcher during the at least one individual search session at least one large collection of objects comprising at least one set of object characteristics, each object comprising at least one subset of the at least one set of object characteristics;
• b) wherein the large collection of objects results from one search query of the at least one user searcher;
• c) gathering searcher overall evaluating relating to each single object of at least one sample subset of the at least one large collection of objects during the at least one individual search session;
• d) wherein the searcher overall evaluating comprises searcher-setting of at least one scalar relevancy-value or of at least one comparative relevancy-value relating to each object of the at least one sample subset during the at least one individual search session;
• e) using the gathered searcher overall evaluating relating to each single object of the at least one sample subset of the at least one large collection of objects, identifying at least one user searcher evaluation subset of relevancy-ordered characteristics relating to the sampled objects during the at least one individual search session;
• f) using the identified at least one user searcher evaluation subset of relevancy-ordered characteristics relating to the sampled objects, identifying at least one relevancy order, approximating a preference function of the at least one user searcher during the at least one individual search session, of the at least one large collection of objects, wherein the approximating the value function of the at least one user searcher comprises iterative application of the algorithm to maximize ε subject to:
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• wherein
• i) i represents the valued object characteristic,
• ii) w represents the at least one overall relevancy weight value,
• iii) D represents an object described by an array of the valued object characteristics, and wherein D is at least one of additive form or multiplicative form, or any combination thereof,
• iv) ε represents the difference between the weighted values of all objects in the at least one large collection of objects,
• v) St represents the set of objects within a scalar category t,
• vi) T represents the total number of scalar categories,
• vii) M represents the total number of the valued object characteristics within the at least one large collection of objects,
• viii) PA and PB indicate a air of relatively compared objects with PA indicating the more preferred, and
• ix) R represents the total number of direct preference statements;
• and
• g) presenting, to the at least one user searcher during the at least one individual search session, at least one first relevancy-ordered hierarchy comprising the at least one large collection of objects, using the identified at least one relevancy order of the at least one large collection of objects.