A system (e.g., content management system), a recommender system, a storage device, and various methods are described herein that improves with the aid of human (expert) judgment the online-learning for item-item similarity (e.g., movie-movie similarity).
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a recommender system; a first graphical user interface (GUI); a storage device; the first GUI is configured to transmit metadata of a media item to the recommender system; the recommender system is configured to use the metadata of the media item and a similarity model to find at least one of: (1) suggested similar items associated with the media item, and (2) suggested items from same series associated with the media item; the recommender system is further configured to send a first fetch command to the storage device to have the at least one of: (1) the suggested similar items associated with the media item, and (2) the suggested items from same series associated with the media item sent to the first GUI; the first GUI is configured to display the at least one of: (1) the suggested similar items associated with the media item, and (2) the suggested items from same series associated with the media item and further configured to enable an operator to correct the at least one of: (1) the suggested similar items associated with the media item, and (2) the suggested items from same series associated with the media item to provide at least one of: (1) corrected similar items associated with the media item, and (2) corrected items from same series associated with the media item; the first GUI is further configured to send the at least one of: (1) the corrected similar items associated with the media item, and (2) the corrected items from same series associated with the media item to the recommender system; and, the recommender system is further configured to use the at least one of: (1) the corrected similar items associated with the media item, and (2) the corrected items from same series associated with the media item to update the similarity model with respect to the media item. 2. The system of claim 1, wherein the first GUI is further configured to enable the operator to rank the suggested similar items when providing the corrected similar items. 3. The system of claim 1, wherein: the first GUI is further configured to enable the operator to label the at least one of: (1) the suggested similar items as being similar or dissimilar, and (2) the suggested items from same series as being similar or dissimilar; and the recommender system is configured to use the labels of the at least one of: (1) the suggested similar items as being similar or dissimilar, and (2) the suggested items from same series as being similar or dissimilar to update the similarity model with respect to the media item. 4. The system of claim 1, wherein: the recommender system is configured to use a machine learning technique to update the similarity model with respect to the media item, wherein the machine learning technique includes a Term Frequency-Inverse Document Frequency (TF-IDF) process, a linear regression process, a logistic regression process, a support vector machine (SVM), artificial neural networks, Bayesian belief networks, random forests, or a combination of these. 5. The system of claim 1, further comprising: a second GUI configured to transmit a request by a consumer for similar items related to the media item to the recommender system; the recommender system configured upon receipt of the request to use the similarity model to calculate similar items to the media item; the recommender system further configured to send a second fetch command to the storage device to have the calculated similar items sent to the second GUI; the storage device configured to send the calculated similar items to the second GUI; and, the second GUI configured to display the calculated similar items for the consumer. 6. The system of claim 5, wherein: the storage device is configured to implement a filtering operation where at least one of (1) only the calculated similar items which are available in a library are sent to the second GUI, and (2) only the calculated similar items which are permitted per one or more restrictions associated with the consumer are sent to the second GUI. 7. The system of claim 5, wherein: the storage device is configured to implement the sending of the calculated similar items to the second GUI by constructing a list of calculated similar items M which has a minimum number of m items while there is a list of operator-curated similar items N which has n items where n>=0 by performing following operations: determine whether m<=n; based on a determination that m is <= to n, construct the list of calculated similar items M with m items from the operator-curated similar items N, and send the calculated similar items M to the second GUI; based on a determination that m is not <= to n, perform following operations: construct an empty list of similar items M with size m; add all items from the operator-curated similar items N to the list of calculated similar items M; request items from the recommender system in order to fully populate the list of calculated similar items M; and, send the calculated similar items M to the second GUI. 8. The system of claim 1, further comprising: a second GUI configured to transmit a request by a consumer for items in a series related to the media item to the recommender system; the recommender system configured upon receipt of the request to use the similarity model to calculate items in the series related to the media item; the recommender system further configured to send a third fetch command to the storage device to have the calculated items in the series related to the media item sent to the second GUI; the storage device configured to send the calculated items in the series related to the media item to the second GUI; and, the second GUI configured to display the calculated items in the series related to the media item for the consumer. 9. The system of claim 8, wherein: the storage device is configured to implement a filtering operation where at least one of (1) only the calculated items in the series which are available in a library are sent to the second GUI, and (2) only the calculated items in the series which are permitted per one or more restrictions associated with the consumer are sent to the second GUI. 10. The system of claim 8, wherein: the storage device is configured to implement the sending of the calculated items in the series to the second GUI by constructing a list of calculated items in the series M which has a minimum number of m items while there is a list of operator-curated items in the series N which has n items where n>=0 by performing following operations: determine whether m<=n; based on a determination that m is <= to n, construct the list of calculated items in the series M with m items from the operator-curated items in the series N, and send the calculated items in the series M to the second GUI; based on a determination that m is not <= to n, perform following operations: construct an empty list of items in the series M with size m; add all items from the operator-curated items in the series N to the list of calculated items in the series M; request items from the recommender system in order to fully populate the list of calculated items in the series M; and, send the calculated items in the series M to the second GUI. 11. The system of claim 1, wherein: the first GUI comprises a first section and at least one of a second section and a third section: the first section having a title field, a title type field, a genres field, and a cast field within which the metadata of the media item is entered by the operator; the second section having a suggested similar titles field which displays the suggested similar items, an other similar titles field which receives manual input regarding a similar item from the operator, and a similar titles field which displays the corrected similar items; and, the third section having a suggested titles in this series field which displays the suggested items in the same series, an other titles in this series field which receives manual input regarding an item in the series from the operator, and a titles in this series field which displays the corrected items from the same series. 12. The system of claim 1, further comprises: a second GUI including a title field in which a media item is entered by a consumer, and at least one of (1) a similar titles field which displays similar items to the media item, and (2) a titles in a series field which displays items in the series; or a second GUI including a title field in which a media item is entered by a consumer, and a similar titles field which displays similar items to the media item and items in the series. 13. A recommender system comprising: a processor; and, a memory that stores processor-executable instructions, wherein the processor interfaces with the memory to execute the processor-executable instructions, whereby the recommender system is operable to: receive, from a first graphical user interface, metadata associated with a media item; use the metadata of the media item and a similarity model to find at least one of: (1) suggested similar items associated with the media item, and (2) suggested items from same series associated with the media item; send, to a storage device, a first fetch command to have the at least one of: (1) the suggested similar items associated with the media item, and (2) the suggested items from same series associated with the media item sent to the first GUI to be corrected by an operator; receive, from the first GUI, at least one of: (1) corrected similar items associated with the media item, and (2) corrected items from same series associated with the media item; use the at least one of: (1) the corrected similar items associated with the media item, and (2) the corrected items from same series associated with the media item to update the similarity model with respect to the media item. 14. The recommender system of claim 13, wherein the second use operation further comprises: use labels provided by the operator indicating at least one of: (1) the suggested similar items as being similar or dissimilar, and (2) the suggested items from same series as being similar or dissimilar to update the similarity model with respect to the media item. 15. The recommender system of claim 13, wherein the recommender system is further operable to: update the similarity model with respect to the media item using a machine learning technique selected from a Term Frequency-Inverse Document Frequency (TF-IDF) process, a linear regression process, a logistic regression process, a support vector machine (SVM), artificial neural networks, Bayesian belief networks, random forests, or a combination of these. 16. The recommender system of claim 13, wherein the recommender system is further operable to: receive, from a second GUI, a request by a consumer for similar items related to the media item; use the similarity model to calculate similar items to the media item; and, send, to the storage device, a second fetch command to have the calculated similar items sent to the second GUI. 17. The recommender system of claim 13, wherein the recommender system is further operable to: receive, from the second GUI, a request by a consumer for items in a series related to the media item; use the similarity model to calculate items in the series related to the media item; and, send, to the storage device, a third fetch command to have the calculated items in the series related to the media item sent to the second GUI. 18. A method in a recommender system, the method comprising: receiving, from a first graphical user interface, metadata associated with a media item; using the metadata of the media item and a similarity model to find at least one of: (1) suggested similar items associated with the media item, and (2) suggested items from same series associated with the media item; sending, to a storage device, a first fetch command to have the at least one of: (1) the suggested similar items associated with the media item, and (2) the suggested items from same series associated with the media item sent to the first GUI to be corrected by an operator; receiving, from the first GUI, at least one of: (1) corrected similar items associated with the media item, and (2) corrected items from same series associated with the media item; using the at least one of: (1) the corrected similar items associated with the media item, and (2) the corrected items from same series associated with the media item to update the similarity model with respect to the media item. 19. The method of claim 18, wherein the second using step further comprises: using labels provided by the operator indicating at least one of: (1) the suggested similar items as being similar or dissimilar, and (2) the suggested items from same series as being similar or dissimilar to update the similarity model with respect to the media item. 20. The method of claim 18, wherein the second using step further comprises: updating the similarity model with respect to the media item using a machine learning technique selected from a Term Frequency-Inverse Document Frequency (TF-IDF) process, a linear regression process, a logistic regression process, a support vector machine (SVM), artificial neural networks, Bayesian belief networks, random forests, or a combination of these. 21. The method of claim 18, further comprising: receiving, from a second GUI, a request by a consumer for similar items related to the media item; using the similarity model to calculate similar items to the media item; and, sending, to the storage device, a second fetch command to have the calculated similar items sent to the second GUI. 22. The method of claim 18, further comprising: receiving, from the second GUI, a request by a consumer for items in a series related to the media item; using the similarity model to calculate items in the series related to the media item; and, sending, to the storage device, a third fetch command to have the calculated items in the series related to the media item sent to the second GUI. 23. A storage device a processor; and, a memory that stores processor-executable instructions, wherein the processor interfaces with the memory to execute the processor-executable instructions, whereby the storage device is operable to: receive, from a recommender system a first fetch command indicating at least one of: (1) a suggested similar items associated with a media item, and (2) a suggested items from same series associated with the media item send, to a first graphical user interface (GUI), the at least one of: (1) the suggested similar items associated with the media item, and (2) the suggested items from same series associated with the media item to be corrected by an operator; receive, from the first GUI, at least one of: (1) corrected similar items associated with the media item, and (2) corrected items from same series associated with the media item; store the at least one of: (1) the corrected similar items associated with the media item, and (2) the corrected items from same series associated with the media item. 24. The storage device of claim 23, wherein the storage device is further operable to: receive, from the recommender system, a second fetch command to have calculated similar items associated with a request by a consumer for similar items related to the media item sent to a second GUI; and, send, to the second GUI, the calculated similar items. 25. The storage device of claim 24, wherein the storage device is further operable to implement the sending of the calculated similar items to the second GUI by implementing a filtering operation where at least one of (1) only the calculated similar items which are available in a library are sent to the second GUI, and (2) only the calculated similar items which are permitted per one or more restrictions associated with the consumer are sent to the second GUI. 26. The storage device of claim 24, wherein the storage device is further operable to implement the sending of the calculated similar items to the second GUI by constructing a list of calculated similar items M which has a minimum number of m items while there is a list of operator-curated similar items N which has n items where n>=0 by performing following operations: determine whether m<=n; based on a determination that m is <= to n, construct the list of calculated similar items M with m items from the operator-curated similar items N, and send the calculated similar items M to the second GUI; based on a determination that m is not <= to n, perform following operations: construct an empty list of similar items M with size m; add all items from the operator-curated similar items N to the list of calculated similar items M; request items from the recommender system in order to fully populate the list of calculated similar items M; and, send the calculated similar items M to the second GUI. 27. The storage device of claim 23, wherein the storage device is further operable to: receive, from the recommender system, a third fetch command to have calculated items in a series associated with a request by a consumer for items in a series related to the media item sent to the second GUI; and, send, to the second GUI, the calculated items in a series. 28. The storage device of claim 27, wherein the storage device is further operable to implement the sending of the calculated items in a series to the second GUI by implementing a filtering operation where at least one of (1) only the calculated items in the series which are available in a library are sent to the second GUI, and (2) only the calculated items in the series which are permitted per one or more restrictions associated with the consumer are sent to the second GUI. 29. The storage device of claim 27, wherein the storage device is further operable to implement the sending of the calculated items in the series to the second GUI by constructing a list of calculated items in the series M which has a minimum number of m items while there is a list of operator-curated items in the series N which has n items where n>=0 by performing following operations: determine whether m<=n; based on a determination that m is <= to n, construct the list of calculated items in the series M with m items from the operator-curated items in the series N, and send the calculated items in the series M to the second GUI; based on a determination that m is not <= to n, perform following operations: construct an empty list of items in the series M with size m; add all items from the operator-curated items in the series N to the list of calculated items in the series M; request items from the recommender system in order to fully populate the list of calculated items in the series M; and, send the calculated items in the series M to the second GUI. 30. A method in a storage device, the method comprising: receiving, from a recommender system a first fetch command indicating at least one of: (1) a suggested similar items associated with a media item, and (2) a suggested items from same series associated with the media item sending, to a first graphical user interface (GUI), the at least one of: (1) the suggested similar items associated with the media item, and (2) the suggested items from same series associated with the media item to be corrected by an operator; receiving, from the first GUI, at least one of: (1) corrected similar items associated with the media item, and (2) corrected items from same series associated with the media item; storing the at least one of: (1) the corrected similar items associated with the media item, and (2) the corrected items from same series associated with the media item. 31. The method of claim 30, further comprising: receiving, from the recommender system, a second fetch command to have calculated similar items associated with a request by a consumer for similar items related to the media item sent to a second GUI; and, sending, to the second GUI, the calculated similar items. 32. The method of claim 31, wherein the second sending step further comprises: implement a filtering operation where at least one of (1) only the calculated similar items which are available in a library are sent to the second GUI, and (2) only the calculated similar items which are permitted per one or more restrictions associated with the consumer are sent to the second GUI. 33. The method of claim 31, wherein the second sending step further comprises constructing a list of calculated similar items M which has a minimum number of m items while there is a list of operator-curated similar items N which has n items where n>=0 by performing following steps: determining whether m<=n; based on a determination that m is <= to n, constructing the list of calculated similar items M with m items from the operator-curated similar items N, and sending the calculated similar items M to the second GUI; based on a determination that m is not <= to n, perform following steps: constructing an empty list of similar items M with size m; adding all items from the operator-curated similar items N to the list of calculated similar items M; requesting items from the recommender system in order to fully populate the list of calculated similar items M; and, sending the calculated similar items M to the second GUI. 34. The method of claim 30, further comprising: receiving, from the recommender system, a third fetch command to have calculated items in a series associated with a request by a consumer for items in a series related to the media item sent to the second GUI; and, sending, to the second GUI, the calculated items in a series. 35. The method of claim 34, wherein the second sending step further comprises: implementing a filtering operation where at least one of (1) only the calculated items in the series which are available in a library are sent to the second GUI, and (2) only the calculated items in the series which are permitted per one or more restrictions associated with the consumer are sent to the second GUI. 36. The method of claim 34, wherein the second sending step further comprises constructing a list of calculated items in the series M which has a minimum number of m items while there is a list of operator-curated items in the series N which has n items where n>=0 by performing following steps: determining whether m<=n; based on a determination that m is <= to n, constructing the list of calculated items in the series M with m items from the operator-curated items in the series N, and sending the calculated items in the series M to the second GUI; based on a determination that m is not <= to n, perform following steps: constructing an empty list of items in the series M with size m; adding all items from the operator-curated items in the series N to the list of calculated items in the series M; requesting items from the recommender system in order to fully populate the list of calculated items in the series M; and, sending the calculated items in the series M to the second GUI.