Probabilistic recommendation system
First Claim
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1. A computer-implemented method of selecting items to recommend, the method comprising:
- generating a ranked set of candidate items to recommend to a target user based at least partly on item selection actions performed by the target user, the ranked set of candidate items comprising candidate items and associated rankings of the candidate items;
subsequent to said generating, probabilistically varying the rankings of at least some of the candidate items in the ranked set of candidate items automatically by a computer to generate a probabilistically-varied ranked set of candidate items,said probabilistically varying the rankings comprising using a pseudo-random number generator to vary the rankings by an amount that is based at least in part on a number of the item selection actions performed by the target user;
selecting, from said probabilistically-varied ranked set, a first subset of the candidate items to present to the target user; and
subsequent to said selecting, repeating at least said probabilistically varying the rankings and said selecting to thereby select a second subset of the candidate items to present to the target user, wherein the second subset of the candidate items comprises one or more items that are not members of the first subset of the candidate items.
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Abstract
A recommendations system uses probabilistic methods to select, from a candidate set of items, a set of items to recommend to a target user. The methods can effectively introduce noise into the recommendations process, causing the recommendations presented to the target user to vary in a controlled manner from one visit to the next. The methods may increase the likelihood that at least some of the items recommended over a sequence of visits will be useful to the target user. Some embodiments of the methods are stateless such that the system need not keep track of which items have been recommended to which users.
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Citations
28 Claims
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1. A computer-implemented method of selecting items to recommend, the method comprising:
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generating a ranked set of candidate items to recommend to a target user based at least partly on item selection actions performed by the target user, the ranked set of candidate items comprising candidate items and associated rankings of the candidate items; subsequent to said generating, probabilistically varying the rankings of at least some of the candidate items in the ranked set of candidate items automatically by a computer to generate a probabilistically-varied ranked set of candidate items, said probabilistically varying the rankings comprising using a pseudo-random number generator to vary the rankings by an amount that is based at least in part on a number of the item selection actions performed by the target user; selecting, from said probabilistically-varied ranked set, a first subset of the candidate items to present to the target user; and subsequent to said selecting, repeating at least said probabilistically varying the rankings and said selecting to thereby select a second subset of the candidate items to present to the target user, wherein the second subset of the candidate items comprises one or more items that are not members of the first subset of the candidate items. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computer-implemented method of selecting items to recommend, the method comprising:
by a computer system comprising computer hardware; identifying items selected from an electronic catalog by a target user; for each of the identified items, selecting associated items from a table of associated items to produce a set of candidate recommendations, said selecting comprising providing an initial score for each candidate recommendation; for each of the candidate recommendations, identifying a number of the item selections made by the target user that resulted in the candidate recommendation being selected from the table of associated items, and calculating a new score for the candidate recommendation using a value selected from a probability function, the probability function configured to provide the value based at least partly on the identified number of item selections, wherein the probability function at least uses a pseudo-random number generator to calculate the new score; outputting a first subset of the candidate recommendations for presentation to the target user, each of the candidate recommendations being ranked in the output according to the new score for that candidate recommendation; and subsequent to said outputting, repeating at least said calculating the new score for each of the candidate recommendations and said outputting to thereby output a second subset of the candidate recommendations, wherein the second subset of the candidate recommendations comprises at least some different items from the first subset of the candidate recommendations. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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23. A recommendation engine for selecting items to recommend, the recommendation engine comprising:
a computer system comprising one or more processors, said computer system programmed to implement; a candidate generator configured to; generate a ranked set of candidate items to recommend to a target user, the ranked set of candidate items comprising candidate items and associated rankings of the candidate items; a probabilistic scorer configured to, subsequent to the candidate generator generating the ranked set of candidate items, probabilistically vary the rankings of at least some of the candidate items in the ranked set of candidate items to generate a probabilistically-varied ranked set of candidate items, the probabilistic scorer configured to probabilistically vary the rankings by at least using a pseudo-random number generator to vary the rankings by an amount based at least in part on one or more item selection actions performed by the target user; and a candidate filter configured to select, from said probabilistically-varied ranked set, a first subset of the candidate items to present to the user; wherein the probabilistic scorer is further configured to probabilistically vary the rankings of at least some of the candidate items a second time to generate a second probabilistically-varied ranked set of candidate items, and wherein the candidate filter is further configured to select a second subset of the second probabilistically-varied ranked set of candidate items to present to the user, wherein the second subset comprises at least some different items from the first subset of the candidate items. - View Dependent Claims (24, 25)
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26. A method for varying recommendations presented to users, the method comprising:
by a computer system comprising computer hardware; selecting items as candidates for recommendation to a target user; ranking the items using a recommendation process that depends at least in part on item selection actions performed by the target user, the rankings configured to estimate an optimal order for presenting the items as recommendations for the target user; varying the rankings of the items to produce varied rankings by at least using a probabilistic function that depends at least in part on the item selection actions performed by the target user so as to estimate a less optimal order for presenting the items to the target user, the probabilistic function being implemented at least in part by a pseudo-random number generator, such that some of the rankings are increased for some of the items and other of the rankings are decreased for other of the items; outputting a first subset of the items as a most highly-ranked subset of the items according to the varied rankings as additional recommendations for presentation to the target user; and subsequent to said outputting, repeating said selecting, ranking, varying, and outputting, based on the same item selection actions performed by the target user, to thereby output a second subset of the items to recommend to the target user, the second subset of items comprising at least some different items from the first subset of items. - View Dependent Claims (27, 28)
Specification