Software that uses personalized information pertaining to a user to determine how familiar (or “novel” or “surprising”) a new artifact will be to the user, by performing the following steps: (i) receiving the identity of a first user; (ii) receiving a first dataset pertaining to the first user; (iii) receiving the identity of a first artifact; and (iv) applying a probabilistic familiarity algorithm to the first dataset with respect to the first artifact to yield a probabilistic familiarity value for the first artifact with respect to the first user. The first dataset is received over a computer network, and the first dataset includes at least one piece of personalized information for the first user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving the identity of a first user; receiving a first dataset pertaining to the first user; receiving the identity of a first artifact; and applying a probabilistic familiarity algorithm to the first dataset with respect to the first artifact to yield a probabilistic familiarity value for the first artifact with respect to the first user; wherein: the first dataset is received over a computer network; and the first dataset includes at least one piece of personalized information for the first user. 2. The method of claim 1, further comprising: determining if the probabilistic familiarity value is below a first probabilistic familiarity threshold; wherein if the probabilistic familiarity value is below the first probabilistic familiarity threshold, the first artifact is added to a set of artifact(s) that are not familiar to the first user. 3. The method of claim 1, wherein: the personalized information includes at least one of the following: activity by the user on a social networking service (SNS); activity viewable by the user on a SNS; online purchase history of the user; online browser history of the user; blog posts by the user; website content created by and/or maintained by the user; user email content; user online chat content; and/or content created by a third party but attributing portions to the user. 4. The method of claim 1, wherein: the probabilistic familiarity algorithm is a Bayesian surprise-based algorithm. 5. The method of claim 1, wherein: the first dataset further includes at least one piece of personalized information for a first social networking connection of the first user, where the user is connected to the first social networking connection on a first social networking service. 6. The method of claim 5, wherein: the first dataset further includes at least one piece of personalized information for a second social networking connection, where the second social networking connection is connected to the first social networking connection on the first social networking service. 7. A computer program product comprising a computer readable storage medium having stored thereon: first program instructions programmed to receive the identity of a first user; second program instructions programmed to receive a first dataset pertaining to the first user; third program instructions programmed to receive the identity of a first artifact; and fourth program instructions programmed to apply a probabilistic familiarity algorithm to the first dataset with respect to the first artifact to yield a probabilistic familiarity value for the first artifact with respect to the first user; wherein: the first dataset is received over a computer network; and the first dataset includes at least one piece of personalized information for the first user. 8. The computer program product of claim 7, further comprising: fifth program instructions programmed to determine if the probabilistic familiarity value is below a first probabilistic familiarity threshold; wherein if the probabilistic familiarity value is below the first probabilistic familiarity threshold, the first artifact is added to a set of artifact(s) that are not familiar to the first user. 9. The computer program product of claim 7, wherein: the personalized information includes at least one of the following: activity by the user on a social networking service (SNS), activity viewable by the user on a SNS, online purchase history of the user, online browser history of the user, blog posts by the user, website content created by and/or maintained by the user, user email content, user online chat content, and/or content created by a third party but attributing portions to the user. 10. The computer program product of claim 7, wherein: the probabilistic familiarity algorithm is a Bayesian surprise-based algorithm. 11. The computer program product of claim 7, wherein: the first dataset further includes at least one piece of personalized information for a first social networking connection of the first user, where the user is connected to the first social networking connection on a first social networking service. 12. The computer program product of claim 11, wherein: the first dataset further includes at least one piece of personalized information for a second social networking connection, where the second social networking connection is connected to the first social networking connection on the first social networking service. 13. A computer system comprising: a processor(s) set; and a computer readable storage medium; wherein: the processor set is structured, located, connected and/or programmed to run program instructions stored on the computer readable storage medium; and the program instructions include: first program instructions programmed to receive the identity of a first user; second program instructions programmed to receive a first dataset pertaining to the first user; third program instructions programmed to receive the identity of a first artifact; and fourth program instructions programmed to apply a probabilistic familiarity algorithm to the first dataset with respect to the first artifact to yield a probabilistic familiarity value for the first artifact with respect to the first user; wherein: the first dataset is received over a computer network; and the first dataset includes at least one piece of personalized information for the first user. 14. The computer system of claim 13, further comprising: fifth program instructions programmed to determine if the probabilistic familiarity value is below a first probabilistic familiarity threshold; wherein if the probabilistic familiarity value is below the first probabilistic familiarity threshold, the first artifact is added to a set of artifact(s) that are not familiar to the first user. 15. The computer system of claim 13, wherein: the personalized information includes at least one of the following: activity by the user on a social networking service (SNS), activity viewable by the user on a SNS, online purchase history of the user, online browser history of the user, blog posts by the user, website content created by and/or maintained by the user, user email content, user online chat content, and/or content created by a third party but attributing portions to the user. 16. The computer system of claim 13, wherein: the probabilistic familiarity algorithm is a Bayesian surprise-based algorithm. 17. The computer system of claim 13, wherein: the first dataset further includes at least one piece of personalized information for a first social networking connection of the first user, where the user is connected to the first social networking connection on a first social networking service. 18. The computer system of claim 17, wherein: the first dataset further includes at least one piece of personalized information for a second social networking connection, where the second social networking connection is connected to the first social networking connection on the first social networking service.